Job Matches for Skills

Browse jobs that match your experience to a client's hiring preferences. Ordered by most relevant. Posted 23 minutes ago Python Engineer | Agentic AI & Automation, LLM Apps, RAG Pipelines Hourly: $19-$40 - Intermediate - Est. Time: 1 to 3 months, Less than 30 hrs/week I am looking for a Python Engineer with strong experience in Agentic AI systems, LLM-based applications, and automation. You will work on building intelligent workflows using LLMs, developing RAG (Retrieval-Augmented Generation) pipelines, and automating complex processes. This role is ideal for someone who understands how to design, optimize, and scale AI-powered systems in real-world applications. Responsibilities Build LLM-powered applications using Python Design and implement Agentic AI workflows Develop and optimize RAG pipelines Integrate vector databases and embeddings Automate workflows and backend processes Improve performance, reliability, and accuracy of AI systems moreabout "Python Engineer | Agentic AI & Automation, LLM Apps, RAG Pipelines" Skills Skip skills Python Artificial Intelligence Amazon Web Services API Machine Learning DevOps Java TensorFlow Verified Payment verified Rating is 5.0 out of 5. $2K+ spent Pakistan Proposals: 5 to 10 Posted 3 hours ago AI Developer – Domain-Specific Chatbot (Document-Based) Hourly: $20-$45 - Intermediate - Est. Time: 3 to 6 months, Less than 30 hrs/week We want to build a domain-specific chatbot that answers questions only from our internal documents, not from general internet knowledge. This is not a generic ChatGPT wrapper. The chatbot must be grounded in uploaded data and provide accurate, traceable answers. Requirements - Build a chatbot that works on a specific domain knowledge base - Document ingestion (PDFs, text, scanned files if needed) - Text chunking, embeddings, and retrieval (RAG-style approach) - LLM integration (OpenAI or similar) with hallucination control - Answers must be based strictly on provided documents - API-first backend (Python preferred) - Optional simple web-based chat UI Skills Required - Python backend development - OpenAI API or LLM integration - Vector databases (FAISS, Pinecone, etc.) - Document parsing and preprocessing - Experience with domain-specific or knowledge-based chatbots This is a serious build intended for real users, not a demo. Clear communication, system-level thinking, and production experience are important. moreabout "AI Developer – Domain-Specific Chatbot (Document-Based)" Skills Python OpenAI Embeddings AI Chatbot Natural Language Processing (NLP) OpenAI API Vector Database Unverified Payment unverified Rating is 0 out of 5. $0 spent Saudi Arabia Proposals: 50+ Featured Posted yesterday AI Founding Engineer – SEO/GEO & Humanization Specialist Hourly: $15-$40 - Expert - Est. Time: More than 6 months, 30+ hrs/week ## AI Founding Engineer – SEO/GEO & Humanization Specialist ## Important Notice: - Individual developers only – We do NOT accept applications from agencies or consulting firms - This is a full-time position through Upwork requiring exclusive commitment - No freelancers or part-time arrangements will be considered - You will be required to sign an NDA stipulating that no other professional projects can be undertaken during employment ## About AISEO AISEO is pioneering the intersection of generative AI and search optimization, building cutting-edge tools that revolutionize how content ranks in both traditional search engines and AI-powered answer engines. We're at the forefront of Generative Engine Optimization (GEO), helping thousands of businesses adapt to the AI-first search landscape while maintaining authentic, human-quality content. ## Position Overview We're seeking an exceptional Senior Generative AI Engineer with deep expertise in SEO/GEO and model humanization to architect and build the next generation of AI-powered content optimization tools. You'll lead the development of sophisticated backends that power our suite of SEO tools, fine-tune LLMs for human-like content generation, and pioneer GEO strategies that help content rank in AI-generated responses. This role requires someone who understands both the technical depth of AI systems and the strategic nuances of modern search optimization. You'll be building systems that not only generate content but ensure it performs exceptionally in both traditional SERPs and AI answer engines. ## Key Responsibilities ### AI Model Development & Humanization - Fine-tune and optimize LLMs (GPT-4, Claude, Llama, Mistral) specifically for SEO-optimized, human-like content generation - Develop sophisticated humanization pipelines that bypass AI detection while maintaining content quality and SEO performance - Implement advanced prompt engineering strategies for consistent, brand-aligned content at scale - Create custom model adapters using LoRA/QLoRA for domain-specific SEO tasks - Build evaluation frameworks to measure content authenticity, readability, and SEO effectiveness ### Backend Architecture & Tool Development - Design and build scalable microservices backends for our suite of SEO/GEO tools (Node.js/Python) - Develop APIs that power keyword research, content optimization, SERP analysis, and competitor intelligence tools - Implement real-time content scoring and optimization engines - Build robust webhook systems for third-party integrations (Search Console, Analytics, etc.) - Create distributed task queues for handling large-scale content generation and analysis ### SEO & GEO Innovation ## Pioneer Generative Engine Optimization techniques to maximize visibility in AI-generated search results - Develop algorithms that predict and optimize for AI answer engine preferences - Build systems that analyze how different AI models interpret and rank content - Create tools that optimize content for featured snippets, knowledge graphs, and AI overviews - Implement advanced semantic analysis for entity recognition and topical authority mapping ### RAG & Knowledge Systems - Design retrieval-augmented generation systems optimized for SEO data sources - Build vector databases for semantic search across millions of keywords and SERPs - Implement knowledge graphs that capture SEO relationships and ranking factors - Create multi-modal RAG systems that incorporate SERP features, images, and structured data ## Required Qualifications ### Technical Expertise - 5+ years backend engineering experience with production AI systems - Expert-level understanding of transformer architectures and fine-tuning techniques - Proven experience building and deploying LLM-powered applications at scale - Deep knowledge of SEO principles, ranking factors, and search algorithms - Hands-on experience with model humanization and AI detection bypass techniques - Strong proficiency in Python (FastAPI, Django) and Node.js (Express, NestJS) ### AI & ML Skills - Experience fine-tuning models with custom datasets (Hugging Face, PyTorch, TensorFlow) - Expertise in prompt engineering and few-shot learning techniques - Knowledge of quantization, distillation, and model optimization techniques - Experience with vector databases (Pinecone, Weaviate, Qdrant) - Familiarity with MLOps tools (MLflow, Weights & Biases, Ray) ### SEO & GEO Knowledge - Understanding of modern SEO: E-E-A-T, Core Web Vitals, semantic search - Experience with SEO APIs (Search Console, SEMrush, Ahrefs) - Knowledge of how AI models process and rank content - Understanding of structured data, schema markup, and knowledge graphs - Familiarity with content optimization for AI overviews and featured snippets ### Infrastructure & DevOps - Experience with cloud platforms (GCP preferred, AWS/Azure acceptable) - Kubernetes orchestration for ML workloads - CI/CD pipelines for model deployment - Monitoring and observability for AI systems - Cost optimization for GPU/TPU workloads ## Preferred Qualifications - Published research or significant open-source contributions in NLP/AI - Experience with GEO (Generative Engine Optimization) strategies - Background in information retrieval or search systems - Knowledge of multiple LLM providers (OpenAI, Anthropic, Cohere, etc.) - Experience with multi-agent AI systems - Understanding of content licensing and AI copyright considerations ## Project Focus Areas You'll be working on cutting-edge projects including: - Humanization Engine: Advanced model fine-tuning for undetectable AI content - GEO Optimizer: Tools that optimize content for AI answer engines - SERP Intelligence Platform: Real-time analysis of search results and AI responses - Semantic Content Studio: AI-powered content creation with built-in SEO optimization - Competitive AI Analyzer: Track how competitors leverage AI for SEO - Multi-Model Orchestrator: Intelligently route tasks to optimal AI models ## Employment Requirements - Available to work exclusively for AISEO (no side projects or consulting) - Must sign NDA and employment contract - Available during Amsterdam business hours - Strong written and verbal English communication skills ## Why Join AISEO? - Impact at Scale Your work will power tools used by thousands of marketers and millions of end users daily, directly influencing how content ranks in the AI era. - Technical Innovation Work with the latest LLMs, experiment with cutting-edge techniques, and define best practices for GEO. - Career Growth As we double year-over-year, leadership opportunities emerge rapidly. Shape the future of AI-powered SEO. - Culture Fast-paced, results-driven, technically excellent. We value deep work, innovation, and shipping quality products. ## How to Apply ### Required Application Materials Subject Line: "Senior GenAI-SEO Engineer – \[Your Name\]" 1\. Technical Portfolio (Required) - GitHub repositories showcasing AI/ML projects - Examples of fine-tuned models or humanization techniques - Backend systems you've architected - Any published papers or technical blog posts 2\. Cover Letter (500 words max) Please address: - Your experience fine-tuning LLMs for specific use cases - A technical challenge you solved involving AI and content/SEO - Your understanding of GEO and how AI is changing search - How you would approach building a humanization system that maintains SEO value - Your vision for the future of AI in search optimization 3\. Case Study Describe in detail one of the following: - An AI system you built that processed or generated content at scale - How you optimized model performance for production use - A fine-tuning project and its measurable outcomes 4\. Resume Highlighting: - Relevant AI/ML projects with quantifiable impact - SEO or content-related technical work - Backend systems and their scale/performance metrics - Open-source contributions or publications moreabout "AI Founding Engineer – SEO/GEO & Humanization Specialist" Skills Skip skills Generative AI Generative AI Prompt Engineering Search Engine Optimization finetuning AI Agent Development SEO Content Verified Payment verified Rating is 5.0 out of 5. $30K+ spent Netherlands Proposals: 5 to 10 Posted yesterday RAG Consultant - turn Standard Operating Procedures into AI Knowledge Assets Hourly - Intermediate - Est. Time: Less than 1 month, Less than 30 hrs/week Retrieval-Augmented AI (RAG) Consultant Insurance Operations Knowledge System Project Overview We are seeking an experienced Retrieval-Augmented Generation (RAG) consultant to help us convert 30 years of independent insurance agency SOPs, best practices, and system workflows into a secure, enterprise-grade AI knowledge system. This is NOT a general chatbot project. The goal is to build a grounded, auditable AI assistant that answers operational questions using our approved documentation, terminology, and workflows. moreabout "RAG Consultant - turn Standard Operating Procedures into AI Knowledge Assets" Skills Skip skills React PHP Vue.js Laravel Node.js WordPress Shopify LLM Prompt Engineering AI Platform Python Verified Payment verified Rating is 5.0 out of 5. $400+ spent United States Proposals: 50+ Posted yesterday Build an SMS-Based AI Conversation Prototype (MVP) Hourly: $35-$45 - Intermediate - Est. Time: 1 to 3 months, Less than 30 hrs/week United States only I am looking for a developer to help build a lightweight SMS based AI system that can send and receive text messages and respond conversationally using an AI interface. This is an early stage prototype, not a finished SaaS, and the goal is to prove basic functionality and flow rather than over engineer a solution. At a high level, the system should send periodic outbound SMS messages, receive replies, and generate appropriate conversational responses through an AI model. It should handle basic logic such as no response scenarios, simple escalation rules like sending a follow up message, and logging conversations. This is not a medical, emergency, or compliance heavy project and should explicitly avoid anything related to crisis lines, harm prevention, or regulated use cases. I am intentionally keeping requirements flexible. I am looking for someone comfortable helping shape the approach, choosing practical tools or APIs, and building something clean, simple, and understandable. Experience with SMS platforms, backend logic, and AI model integration is helpful. This project may evolve into additional work if the prototype is successful. moreabout "Build an SMS-Based AI Conversation Prototype (MVP)" Skills twillo LLM Prompt SMS Verified Payment verified Rating is 5.0 out of 5. $200+ spent United States Proposals: 20 to 50 Posted yesterday AI Agent & SaaS Developer to Build a Voice-Enabled Automation Platform Hourly: $25 - Entry level - Est. Time: Less than 1 month, Less than 30 hrs/week We are building a B2B AI platform that helps businesses automate customer interactions and internal workflows using AI agents (chat and voice). The goal is to create a production-ready SaaS, starting with an MVP and evolving into a scalable product used by real customers. What we are building: AI agents that answer questions, handle conversations, and trigger actions Optional voice layer for phone calls and bookings Web dashboard to manage agents, data sources, and users Secure and scalable backend Preferred tech stack: Python LLMs (OpenAI, Claude or similar) RAG (LangChain, LangFlow or equivalent) React and Next.js PostgreSQL Node.js for integrations if needed Voice AI (Twilio, ElevenLabs, LiveKit) optional but a plus Docker AWS or GCP What we are looking for: Experience building AI agents or AI-powered SaaS products Hands-on work with LLMs and RAG Strong system and architecture thinking Ability to work independently and suggest improvements Clear communication Project stage: MVP with long-term collaboration potential Focus on quality and maintainability over speed How to apply: Share relevant AI or SaaS projects Explain your role in those projects Mention any experience with voice AI or automation moreabout "AI Agent & SaaS Developer to Build a Voice-Enabled Automation Platform" Skills Skip skills Artificial Intelligence JavaScript API Python Chatbot Development Twilio API Machine Learning Python + LLM + RAG Verified Payment verified Rating is 5.0 out of 5. $800+ spent Ukraine Proposals: 50+ Posted 1 hour ago Python AI Job Apply Agent (Playwright): Handle New Tab Redirects & External Site Automation Fixed-price - Expert - Est. Budget: $250 I need a Python developer to build a background AI agent for my job platform, Scowter. The Workflow: 1. The Agent visits a specific job page on Scowter. 2. It clicks the "Apply" button. 3. Crucial: The button opens the external job link in a NEW TAB. 4. The Agent must detect this new tab/popup, switch context to it, and interact with the external site (using AI/LLM to parse the page). Requirements: • Python & Playwright: You must be comfortable using page.expect_popup() to catch the new tab event. • LLM Integration: Use OpenAI/LangChain to understand the external site's layout. • Stealth Mode: Implementation of playwright-stealth to avoid detection on external sites. moreabout "Python AI Job Apply Agent (Playwright): Handle New Tab Redirects & External Site Automation" Skills Automation Python AI Agent Development Verified Payment verified Rating is 4.7 out of 5. $5K+ spent India Proposals: Less than 5 Posted 8 hours ago Build a RAG system to generate application skeletons from Markdown specs Fixed-price - Expert - Est. Budget: $300 Description We are a software studio building modular business applications (“bricks”) inside a shared repository. Each brick is defined by a detailed Markdown specification and follows strict conventions. Our goal is to build a Retrieval-Augmented Generation (RAG) system that can generate a full brick skeleton from a new specification in order to accelerate development. This is more than backend: the generated output must cover application structure, not just APIs. What we need - We’re looking for someone who can help us: - design a clean RAG architecture, - ingest existing specs + code, - generate a coherent, dev-ready skeleton aligned with existing patterns, - avoid generic or hallucinated outputs. This is a code generation / dev tooling project, not a chatbot. Profile we’re looking for - Experience with RAG systems in real projects - Strong software engineering background (not prompt-only) - Able to think in terms of architecture, conventions, and reuse How to apply: - a short explanation of how you would approach this problem, - relevant past experience (RAG, code generation, dev tooling). moreabout "Build a RAG system to generate application skeletons from Markdown specs" Skills AI-Generated Code AI App Development AI Agent Development Web Application TypeScript Verified Payment verified Rating is 0 out of 5. $1K+ spent FRA Proposals: 5 to 10 Posted 7 minutes ago AI Developer Needed for Long-Term Project (LLMs, RAG, Python, React/Next.js) Hourly: $20-$40 - Expert - Est. Time: 3 to 6 months, 30+ hrs/week We are looking for a dedicated AI Developer to join us on a long-term project building and scaling AI-driven features for our product. This is not a one-off task. We want someone who can grow with the product and take ownership of AI development over time. The work involves building LLM-powered applications, AI chatbots, and conversational AI systems that work with real user data and real business use cases. You will work with Large Language Models (LLMs) such as OpenAI (GPT-4 / GPT-4o), Claude, Gemini, LLaMA, or Mistral, and implement Retrieval Augmented Generation (RAG) using vector databases like Pinecone, FAISS, ChromaDB, Weaviate, or Milvus. A major part of the role is connecting LLMs with PDFs, documents, and internal knowledge bases to build reliable question-answering systems and semantic search. The role requires strong backend development skills using Python (FastAPI or Flask) and/or Node.js, with experience integrating AI APIs into scalable backend services. Familiarity with LangChain, LlamaIndex, or similar AI frameworks is important. You should be comfortable with prompt engineering, embeddings, and improving AI output quality in production environments. Experience building AI chatbots, NLP applications, virtual assistants, or AI SaaS products is highly valued. Frontend experience with React or Next.js, along with full-stack AI development, is a plus. We also value experience with cloud deployment, Docker, and platforms like AWS, GCP, or Azure. This role is intended to be long-term, with ongoing feature development, improvements, and scaling. We are looking for someone reliable, proactive, and interested in building real AI products over time. Please share: Examples of AI or chatbot projects you have worked on Your experience with LLMs and AI frameworks Your availability for long-term collaboration moreabout "AI Developer Needed for Long-Term Project (LLMs, RAG, Python, React/Next.js)" Skills Skip skills Next.js Python Vector Databases AI Chatbot Large Language Models Artificial Intelligence React LangChain / LlamaIndex Verified Payment verified Rating is 5.0 out of 5. $5K+ spent Canada Proposals: 10 to 15 Posted yesterday AI Chatbot Developer for Real Estate Agency Websites Fixed-price - Expert - Est. Budget: $500 We are seeking an experienced AI chatbot developer to create a custom, embeddable AI-powered chatbot specifically designed for real estate agency websites. The ideal candidate will have a strong background in developing chatbots that enhance user experience and streamline client interactions. You should be able to integrate the chatbot seamlessly into existing website frameworks and provide ongoing support and updates. If you have a passion for real estate and AI technology, please apply with examples of your previous work. Required Features: Powered by OpenAI/ChatGPT (or similar LLM) for natural conversations. Embeddable on any website (provide simple script/code for integration). Interactive chat: Asks about buyer/renter needs (budget, location, beds/baths, house vs. apartment, etc.). Recommends and highlights matching properties from a list/database. Dynamically generates promotional text (e.g., catchy headlines/descriptions like "Check out this stunning modern apartment with city views!"). Displays property details, images, prices, and links. Lead capture: Collects contact info (name, email, phone) and qualifies leads. Optional: Schedule viewings via calendar integration or email notification. Admin panel for agencies to upload/update listings (CSV, Google Sheets, or simple form). Mobile-friendly and 24/7 responsive. Tech Preferences: Tools like LangChain, Voiceflow, Botpress, Dialogflow, or custom with OpenAI API. Clean, professional design (customizable colors/logo). Deliverables: Fully working chatbot. Embedding instructions. Basic training/data setup guide. Source code (if custom) and any API keys setup. Experience Needed: Proven work with AI chatbots, especially real estate or e-commerce. Portfolio/examples of similar projects. moreabout "AI Chatbot Developer for Real Estate Agency Websites" Skills Chatbot Development JavaScript Web Development Python Lead Generation Unverified Payment unverified Rating is 0 out of 5. $0 spent France Proposals: 20 to 50 Posted yesterday AI Workflow Builder (n8n) + AI Content Automation Specialist Hourly: $5-$30 - Intermediate - Est. Time: More than 6 months, 30+ hrs/week We’re looking for a skilled AI Workflow Builder / AI Automation Developer to help us design, implement, and scale AI-powered workflows using n8n. This role is a mix of technical automation and AI content systems. You should be comfortable building workflows that generate UGC-style content (static + video) using AI models, APIs, and third-party tools, and deploying those workflows inside our own n8n account. If you already have existing n8n workflows you’ve built and are willing to customize or license them, that’s a big plus. This starts as a paid trial and can evolve into a long-term partnership. What You’ll Do Build and optimize n8n workflows inside our account Create AI pipelines that generate: UGC-style static images Short-form video content Connect AI models, APIs, and media tools (image, video, voice, text) Help structure repeatable, scalable automation for content production Document workflows so they’re understandable and reusable Trial Structure (Paid) We will start with a two-part paid trial: Create one AI-generated UGC video for our brand Build and deploy the workflow that produced it inside our n8n account If the trial is successful, we’ll continue working together on additional workflows and long-term automation. What We’re Looking For Strong experience with n8n (required) Experience building AI workflows, not just prompting Familiarity with AI image and video generation tools Ability to think in systems and automation, not one-off tasks Comfortable working inside a client’s environment and documenting work Nice to Have Existing n8n workflows you’ve already built Experience with UGC or performance marketing content Knowledge of API integrations, webhooks, and data flows moreabout "AI Workflow Builder (n8n) + AI Content Automation Specialist" Skills Skip skills AI Content Creation AI Content Writing AI Audio Generation AI Text-to-Image AI Video Generation Artificial Intelligence Verified Payment verified Rating is 4.9 out of 5. $10K+ spent United States Proposals: 20 to 50 Posted yesterday AI consultant for streamlining operations Hourly: $70-$125 - Expert - Est. Time: 1 to 3 months, Less than 30 hrs/week United States only Looking for someone to help us choose a direction for implementation of AI to streamline our operations. We are looking for someone to help us tie all of our systems together, take away small tasks, and show us possible new ideas for AI Skills Skip skills AI Consulting Business Operations Data Analysis AI Agent Development AI Builder Artificial Intelligence Project Management Verified Payment verified Rating is 0 out of 5. $4K+ spent USA Proposals: 50+ Posted 1 hour ago Lead AI Automation & Multimodal Agent Engineer Fixed-price - Expert - Est. Budget: $715 We are looking for a high-level Senior AI & Automation Engineer to architect and build next-generation Multimodal Agentic Workflows. We need someone who understands that modern automation isn't just about moving data from A to B, but about using Large Language Models (LLMs) to make decisions, categorize content, and handle unstructured data autonomously. An expert who builds autonomous systems capable of reasoning, decision-making, and performing complex Generative AI tasks (Image, Voice, and Video) within a unified automation framework. Key Responsibilities Workflow Architecture: Design and implement complex workflows in n8n, Zapier, or Make.com. LLM Integration: Integrate OpenAI (GPT-4o), Claude 3.5, or Gemini into workflows for intelligent decision-making and content processing. Agentic Frameworks: Build systems where AI agents can "use tools" (API calls, web searches) to complete multi-step tasks. Data Routing: Using AI for classification and sentiment analysis to route leads or support tickets within our stack (e.g., HubSpot/Slack). API Management: Connecting custom endpoints and managing authentication between various SaaS platforms. Multimodal Generation: Integrate generative pipelines for: Image: Stable Diffusion, Midjourney, or DALL-E 3. Voice: ElevenLabs, Play.ht, or OpenAI TTS. Video: HeyGen, Runway, or Sora. Autonomous Agents: Architect agents that use tools, browse the web, and execute multi-step logic autonomously. LLM & Vision Integration: Implement reasoning chains using GPT-4o, Claude 3.5, and vision models for media analysis. Backend & Infrastructure: Maintain APIs using FastAPI/Django and manage memory via Vector Databases (Pinecone, Weaviate). Required Skills & Experience Expertise in n8n (custom nodes, advanced expressions) and Python-based automation. Deep experience with Generative AI APIs across all modalities (Text, Image, Audio, Video). Strong backend engineering: FastAPI/Django, Docker, and robust API design. Proficiency in NLP/CV libraries (OpenCV, YOLO, Transformers, LangChain). Experience building Autonomous Agents with recursive logic or specialized frameworks (CrewAI, AutoGPT). Strong understanding of JSON, Webhooks, and REST APIs. Experience with RAG (Retrieval-Augmented Generation) and vector databases is a major plus. Application Instructions Please start your proposal with the word "MULTIMODAL" so I know you’ve read the full description. In your proposal, please answer: Describe an autonomous agent you've built that uses external tools to complete a goal. How do you pipeline image or video generation into a high-volume automated workflow? What is your strategy for managing state/memory in multi-stage agentic loops? moreabout "Lead AI Automation & Multimodal Agent Engineer" Skills Python Data Scraping Artificial Intelligence Lead Generation Data Mining Verified Payment verified Rating is 5.0 out of 5. $40K+ spent Canada Proposals: 20 to 50 Posted yesterday Python | AI/ML & LLMs | Back-end | Cloud | Scraping | Full-Stack | n8n Hourly - Intermediate - Est. Time: Less than 1 month, Less than 30 hrs/week Product Vision Worksy.ai is an AI-driven recruitment matching platform designed to reduce hiring inefficiencies such as long hiring cycles, poor candidate–job fit, and high recruitment costs. This MVP is not a final product but a validation tool focused on proving value and scalability. MVP Scope Core users include Candidates, Employers, and an Admin (Founder). The platform is a browser-based web application with authentication, AI-driven matching, and an admin panel. Core Features - Secure login and role-based accounts - Candidate CV upload and profile creation - Employer job posting creation - Automated AI match scoring (0–100) - Admin dashboard for full system oversight AI & Matching Specifications The AI engine analyzes CVs and job descriptions using NLP via OpenAI API or open-source tools such as spaCy or Hugging Face. All AI prompts and outputs must be logged for transparency and improvement. Technical Requirements Backend: Python (FastAPI or Flask) Frontend: HTML/CSS/JS or basic React Database: PostgreSQL or MySQL Infrastructure: Git, Docker, test and production readiness Deliverables The developer must deliver a fully working MVP, complete source code ownership, Git repository access, basic documentation, and a technical walkthrough. Ownership & Legal All code and logic are the exclusive property of Worksy.ai. No black-box systems or reused proprietary code are allowed. moreabout "Python | AI/ML & LLMs | Back-end | Cloud | Scraping | Full-Stack | n8n" Skills Skip skills Python Django Flask API Development Automation Web Scraping FastAPI Artificial Intelligence Machine Learning Verified Payment verified Rating is 0 out of 5. $50 spent NLD Proposals: 20 to 50 Posted yesterday Add Blog + CMS to Existing Netlify Website (No Rebuild) Hourly: $25-$65 - Intermediate - Est. Time: 1 to 3 months, Less than 30 hrs/week United States only 📌 Project Overview I have a fully designed, live website that was created using Google AI Studio, connected to GitHub, and deployed via Netlify. The frontend design is finished and should NOT be rebuilt. I’m looking for an experienced developer to add a blog system + CMS so I can automate and publish blog posts easily (AI-generated content, markdown, or API-based posting). 🧱 Current Tech Stack Frontend: Built with Google AI Studio Repo: GitHub Hosting: Netlify Static site (no backend yet) 🎯 What I Need I want to add a blog + CMS that meets these requirements: ✅ Works with Netlify + GitHub ✅ No frontend redesign or rebuild ✅ Clean URLs (example: /blog/post-title) ✅ SEO-friendly ✅ Easy to automate blog publishing ✅ Supports markdown or API-based posting ✅ Simple admin/editor experience 🛠️ Preferred Solutions (Open to Suggestions) I’m open to your recommendation, but examples include: Netlify CMS / Decap CMS Headless CMS (Sanity, Contentful, etc.) Markdown-based blog with GitHub commits Webhook / API-friendly setup for AI automation If you recommend a CMS, please explain why it’s best for automation. 📦 Deliverables Blog functionality fully working on my live site CMS or workflow to: Create, edit, and publish posts Support future automation (Zapier / Make / API) Clear instructions or short Loom walkthrough Repo remains connected to GitHub + Netlify 👤 Who I’m Looking For Strong experience with: Netlify GitHub-based workflows Static sites + CMS Comfortable working with an existing codebase Can explain tradeoffs clearly Bonus if you’ve worked with AI-generated content pipelines 🚫 What I Do NOT Want ❌ Full website rebuild ❌ WordPress ❌ Heavy frameworks unless necessary ❌ Over-engineered solutions ⏱️ Timeline & Budget Timeline: Fast turnaround preferred Budget: Open to fixed price or hourly (please estimate) 📩 To Apply, Please Include: Your recommended blog/CMS approach and why Examples of similar Netlify + CMS projects Confirmation you will not redesign the site moreabout "Add Blog + CMS to Existing Netlify Website (No Rebuild)" Skills Blog Website Customization Website Verified Payment verified Rating is 5.0 out of 5. $4K+ spent United States Proposals: 20 to 50 Featured Posted yesterday Founding Engineer / CTO – Copy Intelligence & AI Systems (Genie AI) Hourly - Expert - Est. Time: More than 6 months, 30+ hrs/week 🚀 ABOUT THIS ROLE (READ CAREFULLY) We’re part of the team behind @paulhilse (1.5M+ followers) and are building Genie AI, a next-generation AI content creation platform. This is not a typical CTO role. This is not an infra-only engineering role. This is not a prompt-copying position. We are looking for a rare hybrid: someone who can build AI systems in code and understand persuasion deeply. Your job is to design and own the copy intelligence engine that powers Genie AI. That means translating human judgment, persuasion strategy, and taste into executable systems that consistently produce high-quality copy at scale. This starts as fractional / consulting, but there is clear long-term growth potential. For the right person, this can evolve into a core leadership or CTO role, owning Genie AI end-to-end. We are willing to pay top-of-market for the right talent. 🛠 WHAT YOU’LL ACTUALLY DO Architect and build AI systems that generate persuasive, on-brand copy Design decision logic for audience awareness, intent, and persuasion strategy Implement prompt pipelines, routing, constraints, and evaluation layers Work directly with LLM APIs (OpenAI, Anthropic, etc.) Diagnose why AI outputs fail and fix the system, not just the text Define quality standards for what is and is not shippable Collaborate closely with product and strategy leadership as Genie AI evolves ✅ WHO THIS ROLE IS FOR You are likely a strong fit if: You are a founding-level engineer or CTO-type who has built AI-driven products You can code production systems, not just demos You understand why persuasion works, not just how LLMs work You think in decision engines, constraints, and feedback loops You have strong product judgment and own outcomes You care deeply about quality, taste, and leverage You want long-term upside and to help shape something from the ground up ❌ WHO THIS ROLE IS NOT FOR (PLEASE DON’T APPLY IF…) You are infra-only, backend-only, or DevOps-focused You need extremely detailed specs before moving You mainly identify as a “prompt engineer” You’re looking for short-term freelance work You want to work on small isolated features instead of owning a system We are intentionally selective. 💻 TECHNICAL REQUIREMENTS Strong experience with Python and/or TypeScript Hands-on experience with LLM APIs (OpenAI, Anthropic, etc.) Experience building internal tools, products, or SaaS platforms Ability to design modular systems with clear evaluation logic Comfort iterating quickly and improving systems based on real-world output 🚫 NON-NEGOTIABLES You can explain why AI-generated copy feels generic You can propose a system-level fix, not a rewrite You can articulate tradeoffs and design decisions clearly You take ownership of quality, not just delivery 💎 WHY THIS ROLE IS DIFFERENT You are shaping the core intelligence of Genie AI You’ll work alongside a brand with massive existing distribution Real autonomy and real ownership Clear path to long-term leadership, including CTO Compensation reflects impact. We pay for the best. 📩 HOW TO APPLY (IMPORTANT) To be considered, include: A brief overview of your background Links to products, tools, or systems you’ve built Your experience working with LLMs Answer this question: Why does most AI-generated copy fail, and how would you architect a system to fix it? Applications that skip the question will not be reviewed. ⚠️Please SUBMIT a Loom video answering all the questions above. Applications without a Loom response will not be considered. 📝 FINAL NOTE If you’re the kind of person who: Thinks about how systems think Has both technical depth and persuasion taste Wants to build something meaningful with long-term upside You’ll love this role. See you on the other side. moreabout "Founding Engineer / CTO – Copy Intelligence & AI Systems (Genie AI)" Skills Skip skills AI App Development AI Model Training Prompt Artificial Intelligence Natural Language Processing Machine Learning Verified Payment verified Rating is 5.0 out of 5. $200K+ spent United States Proposals: 20 to 50 Posted yesterday Web Developer / AI Specialist Needed – Personalized AI-Generated Children’s Book Platform Hourly: $20-$50 - Intermediate - Est. Time: 1 to 3 months, Less than 30 hrs/week I’m looking for an experienced web developer with AI integration experience to help build an MVP for a web-based platform that creates fully personalized children’s books using AI. The product allows families to generate custom stories and illustrations based on their own family members (parents, children, pets, etc.), preview the book digitally, and order a physical printed copy via a print-on-demand partner. This is an MVP build with the potential to grow into a full product. moreabout "Web Developer / AI Specialist Needed – Personalized AI-Generated Children’s Book Platform" Skills API JavaScript Python API Integration React Unverified Payment unverified Rating is 0 out of 5. $0 spent Spain Proposals: 20 to 50 Posted 1 hour ago Build Local WhatsApp + AI Workflow to Run My Print Shop (n8n + Local LLM on RTX 3070) Hourly: $20-$40 - Intermediate - Est. Time: 1 to 3 months, Less than 30 hrs/week I run a printing & branding business. I need a real workflow automation system, not just a chatbot. Goal: When a job comes in (mostly via WhatsApp), the system should create a job ticket, assign tasks to staff, track status, and update the customer on WhatsApp automatically – using a LOCAL LLM running on my RTX 3070. What I want built 1. Job intake & tracking Source: WhatsApp Business (and optionally a simple web form/landing page). Every new order becomes a job record with fields like: Job ID Client name & contact Job type (flyer, banner, T‑shirt, signage, etc.) Quantity, size, finishing options Deadline Status (New → In Design → In Print → Finishing → Ready → Delivered) Assigned staff / department Storage can be Google Sheet / Airtable / simple DB – must be easy to view and edit.​2. Automatic routing to staff Routing rules, for example: Design tasks → Designer’s WhatsApp or email Print tasks → Print operator Install jobs → Field/installation team When a job reaches a stage, the system notifies the responsible staff with clear instructions (job ID, requirements, deadline).​Staff must be able to update status quickly (simple link/button, or structured reply that the system understands). 3. Customer updates on WhatsApp When job status changes, the customer receives automatic WhatsApp updates, e.g.: “Your order #1234 is now in design.” “Your order #1234 is being printed.” “Your order #1234 is ready for pickup / out for delivery.”​Customers should be able to message something like “status 1234” and get a current status + basic ETA. 4. LLM / AI usage (MUST be local) Use an LLM to: Turn messy customer WhatsApp messages into clean structured job specs. Generate polite, clear messages for customers and internal notes/instructions for staff.​Important – Local only: The AI/LLM must run on my local RTX 3070 machine (e.g., via Ollama, LM Studio, or similar local server).​Do not hard‑depend on OpenAI or any cloud LLM. You may keep an optional fallback API endpoint, but the core design must work with a local HTTP LLM endpoint on my LAN. Tech stack preference Workflow / orchestration: n8n (preferred), or Make.com / Zapier only if you can justify it clearly for my case.​Messaging: WhatsApp Business Cloud API or a solid WhatsApp provider you know well and can integrate with n8n/Make.​Data storage: Google Sheets / Airtable / or a simple DB – must support listing jobs and changing statuses easily.​AI / LLM: Connect to a local LLM server (Ollama or similar) exposed as an HTTP endpoint, ideally using an OpenAI‑style API.​Design the prompts and flows so that changing the base URL/model is trivial (e.g., http://local-llm:port). Deliverables A working automation that: Creates job records from WhatsApp/form messages. Routes tasks to staff and records their updates. Sends WhatsApp updates to customers at each status change. Uses a local LLM to parse free‑text, generate messages, and help with job specs.​Clear documentation: Diagram of the workflow and data flow. How to modify routing rules, statuses, and message templates. How to change the LLM endpoint (e.g., switch models or ports) without rewriting everything.​What you MUST have experience with Do not apply if you only design basic chatbots. You must show solid past work in at least two of: WhatsApp Business API / WhatsApp automation integrated with CRMs, sheets, or custom backends.​Workflow tools like n8n / Make / Zapier where you move data between tools, trigger actions, and send status notifications.​LLM integration in a real workflow (OpenAI or local) – calling a model via HTTP and using its output to drive logic.​Questions to answer in your first message Which tools do you propose for: WhatsApp integration Workflow orchestration Data storage Local LLM server (name the tool: Ollama, LM Studio, etc.) Send one example of a multi‑step automation you built that: Used WhatsApp (or another chat channel), Wrote/updated records in a sheet/DB, and Sent dynamic status updates back to users.​Have you connected n8n/Make to a local LLM before? If yes, which stack (Ollama, LM Studio, vLLM, etc.)?​Give a rough timeline and price range for delivering a v1 of this system, including one revision cycle. moreabout "Build Local WhatsApp + AI Workflow to Run My Print Shop (n8n + Local LLM on RTX 3070)" Skills n8n API Integration Workflow Automation WhatsApp API LLM Prompt Engineering Verified Payment verified Rating is 4.8 out of 5. $100+ spent United States Proposals: 20 to 50 Posted 2 days ago n8n or Make.com Expert for Workflow Automation & AI Agents Hourly: $30-$45 - Expert - Est. Time: 1 to 3 months, Less than 30 hrs/week Job Description: We are an AI automation agency seeking experienced n8n and/or Make.com experts to build advanced workflows and AI-powered automations for internal use and client projects. You will work on real production automations, not tutorials. Key Responsibilities: Design and build complex automations using n8n and/or Make.com Create end-to-end workflows across tools (CRM, email, databases, APIs) Build and deploy AI agents using OpenAI and other LLMs Integrate tools such as Airtable, Notion, Slack, Google Workspace, CRMs Debug, optimize, and scale automation workflows Document workflows clearly for handoff and maintenance Required Skills & Experience: Strong hands-on experience with n8n and/or Make.com Experience working with APIs and webhooks Experience building AI-powered workflows (OpenAI, prompts, agents) Ability to think in systems and logic, not just connectors Nice to Have: Background in automation agencies or SaaS environments Experience with Airtable, Notion, CRMs, or internal tools Familiarity with data handling, error handling, and scaling workflows Project Type: Ongoing / Long-term Availability: Immediate start To Apply, Please Include: Which platform you specialize in (n8n, Make.com, or both) Examples of complex automations or AI agents you’ve built Tools and APIs you’ve worked with moreabout "n8n or Make.com Expert for Workflow Automation & AI Agents" Skills n8n AI Builder Automation Python JavaScript Verified Payment verified Rating is 0 out of 5. $9K+ spent United States Proposals: 50+ Posted yesterday Full-Stack Web Developer For Surgery Educational Platform Hourly - Intermediate - Est. Time: 1 to 3 months, Less than 30 hrs/week United States only We’re looking for a skilled and motivated full-stack web developer to help prepare an existing healthcare education platform for launch. The project involves unifying two already-built tools into a single, cohesive web application, improving overall architecture, tightening security, and polishing the UI/UX. Much of the code and the core functionalities already exists. The focus should be on reviewing the code, integration, stability, scalability, adding additional features, and take it to production. The ideal candidate is comfortable working with existing codebases, implementing secure authentication and role-based access, adding advanced user analytics, and ensuring that all features work reliably in a real-world launch environment. moreabout "Full-Stack Web Developer For Surgery Educational Platform" Skills Skip skills Machine Learning Data Science Data Analysis Artificial Intelligence BigQuery ETL Pipeline API Integration Unverified Payment unverified Rating is 0 out of 5. $0 spent United States Proposals: 20 to 50 Posted yesterday Software Consultant (Client-Facing, AI-Assisted) Hourly: $30-$40 - Entry level - Est. Time: More than 6 months, 30+ hrs/week United States only We are looking for a Software Consultant to join our team and support client discussions related to software, web, and mobile development projects. This role is client-facing and focuses on communication, clarity, and smart use of AI tools rather than deep hands-on coding. You will act as a bridge between clients and our technical team, ensuring smooth conversations, clear requirements, and professional consulting support. Key Responsibilities - Join video calls with clients to discuss software-related topics - Clearly explain software development concepts using proper technical terminology - Gather and clarify client requirements and relay them to the internal team - Use AI tools (e.g., ChatGPT and similar) quickly and effectively during or after calls to assist with answers, explanations, and summaries - Maintain professional, confident, and friendly communication with clients Requirements - Comfortable with video calls and client-facing communication - Fluent English (spoken and written) - Familiar with common software development terms (web, mobile, backend, frontend, APIs, databases, etc.) - Deep technical expertise is NOT required - Ability to use AI tools seamlessly and efficiently to support discussions and problem-solving - Organized, reliable, and responsive Nice to Have - Previous experience in consulting, tech sales, project coordination, or client support - Experience working with remote teams - Familiarity with SaaS, startups, or software agencies What We Offer - Long-term collaboration opportunity - Flexible working hours - Exposure to real-world software projects and clients - Support from an experienced internal development team moreabout "Software Consultant (Client-Facing, AI-Assisted)" Skills Artificial Intelligence Tech & IT Communications Project Management Verified Payment verified Rating is 5.0 out of 5. $3K+ spent USA Proposals: 10 to 15 Posted yesterday Full Stack AI Automation Engineer Hourly: $25-$42 - Expert - Est. Time: More than 6 months, 30+ hrs/week 🚨 NO AGENCIES. ONLY INDIVIDUAL FREELANCERS/CONTRACTORS. 🚨 If you represent an agency, development shop, or outsourcing firm, please do not apply. We are hiring a dedicated individual team member only. AI Automation Engineer (Full-Stack & Applied AI) Location: Remote or Hybrid (London & San Francisco) Company Stage: Seed / Series A (Raised $10M+ in equity financing) Team: Operations & AI Compensation: Competitive salary Contract Type: Full-Time Why Join CRED? CRED is an AI Native Command Center for businesses. We centralize internal and external company data and pair it with real-time insights to help leaders make better decisions and anticipate the future. With early traction in sports and entertainment—including partnerships with the PGA, Golden State Warriors, and more—and a fresh $10M+ raise, we're scaling fast. We operate one of the world’s most comprehensive business datasets—tracking customers, people, companies, and related signals. We train LLM-powered agents on this data to drive meaningful outcomes for our users across customer support, product development, go-to-market, and more. At CRED, you’ll work on high-impact problems at the intersection of AI, data, and operations. You’ll have ownership, influence, and a direct line to product and leadership. If you thrive in fast-paced environments with tight feedback loops and full-stack thinking, you’ll love it here. What You’ll Be Working On As an in-house AI Automation Engineer, you’ll partner closely with operations, product, and data teams. You won't just be building workflows; you will be engineering robust backend solutions and deploying AI agents that create leverage across the business. Key Initiatives: Applied AI & Agents: Building RAG pipelines, deploying chatbots, and architecting autonomous agents that can reason over our data. Backend Engineering: Deploying REST APIs in Python/Node to support internal projects. Operational Workflows: Automating QA, competitor research, internal documentation, and GitHub project management. Commercial Enablement: Creating seamless data flows for sales, marketing, and partnerships. Who We’re Looking For We are looking for a hybrid engineer: part Backend Developer and part Automation Specialist. You should be obsessed with efficiency and comfortable pair-programming with AI tools to ship faster. Core Technical Requirements: Applied AI : Strong experience with Gen AI trends, including building AI Agents, Chatbots, RAG systems, and working with Vector Databases (e.g., Pinecone, Weaviate). Full-Stack Proficiency (JS/TS): Deep experience with Node.js. You must be comfortable working with GraphQL APIs and building backend services. Python Backend: Ability to write robust Python code for data parsing and deploying REST APIs for various micro-projects. Automation Ecosystem: Expert-level proficiency with tools like n8n, Make.com, and Zapier for orchestrating complex workflows. Enterprise Integration: Proven experience authenticating and working with complex Enterprise APIs (OAuth, rate limits, pagination). Cloud Native: Hands-on experience deploying solutions on AWS, GCP, or Azure. How to Apply (Mandatory Step) To help us filter for the best technical talent and communication skills, please submit a 2-minute Loom video (screen share + camera) along with your application. In the video, please cover: Brief Intro: Your background with full-stack development and applied AI. The Challenge: Walk us through the most difficult AI automation or Agentic workflow you have personally built. Note: Applications without a video will not be reviewed. moreabout "Full Stack AI Automation Engineer" Skills Python JavaScript API Node.js Artificial Intelligence Machine Learning GCP n8n Verified Payment verified Rating is 4.9 out of 5. $200K+ spent GBR Proposals: 20 to 50 Featured Posted 4 hours ago AI Systems Architect (Code Generation & Generative AI) Hourly - Expert - Est. Time: More than 6 months, 30+ hrs/week Title: AI Systems Architect (Generative AI) We are an early-stage startup building an AI platform that generates full-stack web applications from text prompts. Our core engine is nearly complete, and we need a sharp, hands-on architect to help us solve the final systemic challenges and push to launch. This is a high-intensity role for a first-principles thinker who is a pragmatic and brilliant problem-solver. We are looking for a true AI Solutions Architect, not just a senior developer. Requirements: - Demonstrated, elite-level ability to architect complex, production-grade AI systems from the ground up. - Deep, first-principles understanding of multi-agent workflows, state management, and validation pipelines. - Strong foundational knowledge of modern AI/web stacks (Python/Node.js, LLM APIs, React/Next.js). - You must be an individual freelancer available to dedicate yourself full-time to this project. We are not engaging with agencies or freelancers who operate with a team. We are looking for a smart and driven architect to be part of our launch. If this sounds like you, we want to hear from you. How to Apply (Required): To apply, you must answer the following questions in your cover letter. Applications that do not include answers to all questions will be immediately discarded. 1. You are tasked with designing a platform that takes a user's natural language prompt and generates a complete, multi-file, full-stack web application. Provide a high-level component diagram or a clear, detailed description of the data flow for this system. 2. Explain the single most difficult architectural challenge in the system you've just designed, and why your proposed solution is the best way to solve it. 3. Describe the most subtle but critical systemic failure you have experienced in any complex, production-grade software system you have architected. What was the root cause of the failure, and what system-level architectural change did you implement to ensure that entire class of error could not happen again? 4. Describe the single most difficult architectural trade-off you have had to make in your career. Explain the two opposing choices, why you chose one over the other, and what the long-term consequences of that decision were. 5. What is your immediate availability for a full-time role? Do you have any other ongoing projects? 6. What is your expected monthly salary in USD for a full-time position? moreabout "AI Systems Architect (Code Generation & Generative AI)" Skills Artificial Intelligence React Next.js AI Systems Architect Verified Payment verified Rating is 4.5 out of 5. $4K+ spent Hong Kong Proposals: 10 to 15 Featured Posted yesterday AI-native full stack engineer Hourly: $15-$60 - Intermediate - Est. Time: 3 to 6 months, 30+ hrs/week We're looking for an AI-native full stack engineer to work along side our product and project managers to build web apps and services. You have experience building end-to-end web tools and services, both front and backend as well as using services like Vercel and Railway to deploy and manage apps in production. It's a plus if you also have experience with AI automation tools (like N8N) and frameworks (like Claude agent sdk). We're a boutique consulting and product incubation studio so you'll be working across different projects over time. You should be at the bleeding edge of leveraging AI for coding, and you're the klnd of engineer that have made the transition to spend more time thinking, orchestrating and building harnesses for coding agents rather than actually writing code yourself. We have an increasing pipeline of work, so the commitment can be longer term for the right candidate, and even eventually turn into a full time position at a leading nordic AI consulting business. moreabout "AI-native full stack engineer" Skills Skip skills Vercel Web Application AI Agent Development AI Development Full Stack Railway nextjs Python JavaScript Verified Payment verified Rating is 0 out of 5. $700+ spent Norway Proposals: 50+ Posted yesterday AI Creative Automation Developer Hourly: $10-$25 - Intermediate - Est. Time: More than 6 months, Less than 30 hrs/week We're looking for an automation developer to help build AI-powered creative production workflows for e-commerce and agency clients. This is NOT generic AI automation work. You'll be building systems specifically for generating ad creatives at scale - static ads, video ads, UGC-style content. What you'll be building: - Workflows that connect AI tools (Claude, Gemini, Arcads, HeyGen, NanoBanana, ElevenLabs, etc.) - Automated pipelines for competitor ad scraping and analysis - Brief and script generation systems - Quality control and review processes - Custom interfaces for clients to operate the systems Requirements: - Strong experience with n8n (required) - Experience with Gumloop, Poppy AI, or similar (preferred) - Comfortable working with APIs and JSON - Understanding of prompt engineering - Bonus: Familiarity with ad creative tools (Arcads, NanoBanana, HeyGen) This is ongoing work - we're building systems for multiple clients and need someone reliable who can become our go-to for this. moreabout "AI Creative Automation Developer" Skills AI Agent Development n8n gumloop ai creative Generative AI Prompt Engineering Unverified Payment unverified Rating is 0 out of 5. $0 spent United Kingdom Proposals: 10 to 15 Posted yesterday AI Chatbot Developer for Support Automation Fixed-price - Intermediate - Est. Budget: $500 United States only We are seeking an experienced AI Chatbot Developer to create a sophisticated chatbot for automating customer support tasks. The ideal candidate will design and implement an AI-driven solution to enhance our support services, reduce response times, and improve customer satisfaction. You should have a strong understanding of natural language processing, machine learning, and chatbot frameworks. Experience with integration into existing systems is a plus. Join us to revolutionize our customer support experience! moreabout "AI Chatbot Developer for Support Automation" Skills Chatbot Development Artificial Intelligence Natural Language Processing Unverified Payment unverified Rating is 0 out of 5. $0 spent United States Proposals: 10 to 15 Featured Posted yesterday AI Systems Architect – Build a Multi-Agent AI Fulfillment Engine for Marketing Agency Hourly: $15-$35 - Intermediate - Est. Time: 3 to 6 months, 30+ hrs/week We are a fast-growing marketing agency serving health professionals (functional medicine doctors, chiropractors, health coaches). We are hiring an AI Systems Architect to build a production-ready multi-agent AI system that automates 80–90% of our client fulfillment — including research, VSLs, funnels, emails, and ads. This is not a prompt-writing job. This is a real system build used daily in a revenue-generating agency. If you’ve only built demos, chatbots, or single agents — this is not for you. What You Will Build A multi-agent AI fulfillment pipeline, including: Core Agents 1. Onboarding / Intake Agent - Processes onboarding calls, questionnaires, CRM inputs - Market Research Agent - Extracts pains, desires, objections, emotions, dream outcomes 2. Webinar Agent - Generates multiple high-quality Webinar slides and scripts per offer 3. Landing Page & Funnel Copy Agent - Builds full funnel copy (opt-ins, sales pages, checkout) 4. Email & Ascension Agent - Writes abandoned cart, follow-ups, upsells, nurture 5. Advertising Agent - Produces 50–100 ads with unique hooks and angles 6. Optimization Layer - Performance feedback loop - Split-test variation generation - Continuous improvement using campaign data - Data & Training - Fine-tuning / RAG using proprietary datasets - Secure handling of IP - Versioned agent improvements Tech Stack (Flexible) You choose the stack — we care about outcomes. Common tools may include: - Claude (primary writing models) - OpenAI (supporting agents) - Vector databases (RAG) - n8n / Zapier / Make - CRM & funnel integrations (GHL ) Required Experience (Must Have) - Built multi-agent AI systems (not single bots) - Experience with - LLM orchestration - RAG pipelines - Agent routing & automation Strong understanding of: - Direct response marketing - Funnels, VSLs, ads, email - Shipped systems used in real businesses Nice to Have - Agency experience - Meta ads or funnel background - Security-focused mindset (IP protection) How We Hire (Important – Read Carefully) Shortlisted candidates must complete a paid test project. No unpaid work. Ever. To apply, include: - Examples of AI systems you’ve built - Tools/models used - Business impact (time saved, revenue, scale) - A brief note on how you would approach this build Applications without proof of real systems will be ignored. We are not looking for: - Prompt engineers - Chatbot builders - “AI enthusiasts” We are looking for operators who build systems that replace teams. If that’s you — apply. moreabout "AI Systems Architect – Build a Multi-Agent AI Fulfillment Engine for Marketing Agency" Skills Artificial Intelligence Verified Payment verified Rating is 5.0 out of 5. $8K+ spent AUS Proposals: 20 to 50 Posted yesterday AI Automation Engineer to Build Smart Lead Qualification System for Roofing Company (and follow up) Hourly: $50-$100 - Expert - Est. Time: 1 to 3 months, Less than 30 hrs/week Hi, I’m looking for an expert AI automation engineer to help build an AI-powered lead handling system for my roofing company. This system will automate how we engage leads, follow up, make outbound calls, and set appointments. The overall goal is to convert more leads and book more inspections with less manual work. This is not lead generation and not a basic chatbot. It is a backend AI automation system with multiple workflows. WHAT WE NEED BUILT Inbound Lead Handling – Instantly respond to inbound leads (web forms, ads, SMS) – AI-driven SMS or chat conversations – Ask qualifying questions (roof type, location, urgency, insurance vs retail) – Route qualified leads to the correct sales rep – Automatically book inspections on calendars – Send summaries and context to the sales team AI Outbound Calling – AI call agent capable of making outbound calls – Ability to upload or filter a list of leads for outbound calling – Calls focused on setting inspections or follow-ups – Log call outcomes and escalate hot leads to a human rep Storm Outreach – Trigger outreach when storms occur in specific geographic areas – Contact homeowners in affected ZIP codes – Offer post-storm inspections via SMS or calls – Track responses and booked appointments Cold Lead Re-Engagement – Detect leads that stop responding to SMS – Move those leads into monthly email follow-ups – Allow leads to re-enter the active booking flow at any time OVERALL GOAL Automate our lead flows across SMS, calls, and email in order to increase conversion rates, improve follow-up consistency, and set more appointments automatically. REQUIREMENTS – AI-driven conversations (SMS, chat, and voice) – Conditional workflows and logic – CRM integration (AccuLynx preferred; API or webhook-based acceptable) – Calendar booking and rep routing – SMS, call, and email automation – Clear documentation and system handoff This project is mostly backend logic and automation. UI work is minimal. EXPERIENCE NEEDED – Strong experience with AI automation systems – API and webhook integrations – SMS and voice systems (Twilio or similar) – Automation tools (Zapier, Make, or n8n) – Backend development experience (Python or Node.js) This is an expert-level project and not suitable for beginners or marketers. PROJECT DETAILS Scope: Large Timeline: 1–3 months Engagement: Fixed price with milestones Budget: Open. We are well-funded for this project and prioritizing quality, reliability and long-term scalability over the lowest bid. TO APPLY Please include: Examples of similar AI or automation systems you’ve built A brief explanation of how you’d approach this project Estimated timeline and fixed-price proposal. (I have a youtube link that will go into detail sharing more specifics to give you a better road map of what we need done for clarity.) moreabout "AI Automation Engineer to Build Smart Lead Qualification System for Roofing Company (and follow up)" Skills Back-End Development API Integration ai automation workflow automation Verified Payment verified Rating is 5.0 out of 5. $1K+ spent USA Proposals: 50+ Posted yesterday Prompt Engineer Hourly: $50-$100 - Expert - Est. Time: Less than 1 month, not_sure I’m putting together a workflow to make my AI responses feel more production-ready without making things too complicated. If you’re open to it, I’d love to chat and get your take—an outside perspective would really help me tighten up what I’m building. Skills LLM Prompt Engineering Large Language Model Artificial Intelligence Prompt Engineering Verified Payment verified Rating is 0 out of 5. $0 spent United States Proposals: 20 to 50 Posted yesterday Urgent Hourly - Intermediate - Est. Time: Less than 1 month, Less than 30 hrs/week Upwork Title: Voice AI Agent Engineer – Ringover (French-speaking) Job Description: We are looking for an experienced Voice AI Agent Engineer to design and deploy a French-speaking AI phone agent connected to Ringover for ORYNTHA, a premium spiritual / psychic consultation platform. The voice agent will answer inbound calls, create trust with callers, offer optional daily horoscope interaction, explain the offer (including a €10 credit for first-time users), guide users through secure payment, and then transfer the call to a human psychic via Ringover. Responsibilities Design and implement a natural, human-like French voice agent Integrate Ringover telephony APIs (incoming calls, call transfer) Implement AI → human handoff logic (after payment) Build conversation flows focused on trust and reassurance Connect the agent to external APIs (payment, business logic) Test, optimize, and document the solution Required Skills Proven experience building Voice AI / Conversational AI agents Ringover integration experience is mandatory API & webhook integration LLMs (OpenAI or equivalent) Voice UX / call flow design Fluent or native French required Nice to Have Experience with call centers or voice bots Payment flows (Stripe or similar) Experience in wellness / customer care industries Project Details Freelance contract Immediate start Long-term collaboration possible 👉 Please include examples of voice AI agents you’ve built, and clearly explain your experience with Ringover. moreabout "Urgent" Skills Skip skills Artificial Intelligence Machine Learning OpenAI API AI Development Python Retrieval Augmented Generation Verified Payment verified Rating is 0 out of 5. $0 spent France Proposals: 15 to 20

This reads like an Upwork “most relevant” feed for **your exact skill profile**. Given your background (agentic systems, RAG, local LLMs, automation, n8n, Playwright, SEO/GEO, codegen, long-context thinking), here’s a **clear signal-to-noise breakdown** and what I’d actually do if I were you. --- ## 🔥 Top-Tier Matches (Apply First) These align *directly* with what you’ve already built and written about. ### 1. **Python Engineer | Agentic AI & Automation, LLM Apps, RAG Pipelines** **Why it’s a bullseye** - Agentic workflows + RAG + automation = your daily language - Short-term, low-commitment, reasonable rate - Pakistan client, payment verified, low proposal count **Angle to pitch** - Emphasize **decision-making agents**, not “chatbots” - Mention **hallucination control, eval loops, retrieval hygiene** - Talk about **scaling + reliability**, not prompts ✅ **Apply immediately** --- ### 2. **Build a RAG system to generate application skeletons from Markdown specs** **Why it’s perfect** - This is **not a chatbot** - It’s architecture-aware **code generation** - Almost identical to your interests in: - spec → structure - conventions → enforcement - reuse → non-hallucinated output **Angle to pitch** - Talk about: - retrieval over *code + specs* - schema-constrained generation - multi-pass validation (structure → files → content) - Stress you are a **software engineer first**, not a prompt jockey ✅ **High-quality client, low BS** --- ### 3. **Python AI Job Apply Agent (Playwright, new-tab handling)** **Why it’s strong** - Concrete, scoped, technical - Explicitly mentions `page.expect_popup()` (rarely do clients know this) - AI-assisted DOM parsing = your wheelhouse **Angle to pitch** - Demonstrate you understand: - browser context switching - stealth tradeoffs - LLM-as-parser, not LLM-as-brain ✅ **Quick win + credibility boost** --- ## 🟡 Strong but Strategic (Apply If You Want Longer-Term) ### 4. **RAG Consultant – SOPs → Knowledge Assets** - US-based, real ops, not hype - Enterprise mindset, auditable answers - Good fit if you frame it as **knowledge systems**, not AI chat ### 5. **AI Developer Needed for Long-Term Project (LLMs, RAG, Python, React)** - Generic posting, but solid budget and intent - You’ll need to differentiate yourself from 20 “LangChain devs” --- ## 🚫 I’d Skip or Be Careful With These ### ❌ AISEO “Humanization / Detection Bypass” - Red flags: - “bypass AI detection” - NDA + exclusivity - SEO arms race vibes - High effort, high politics, unclear long-term ethics ### ❌ Entry-level n8n / UGC farms - Low leverage - High churn - You’ll be underpaid for systems thinking --- ## 🎯 What You Should Do Next (Concrete) If you want, I can: 1. **Draft 2–3 killer proposals** tailored to: - Agentic AI + RAG role - Codegen-from-specs role - Playwright AI agent role 2. Optimize them to: - Filter out non-technical clients - Signal “architect, not prompt monkey” 3. Keep them **short, confident, and technical** (Upwork rewards clarity, not essays) 👉 Tell me **which 1–3 jobs you want to apply to**, and I’ll write the proposals in your voice.

let's do a coding project which would be a good portfolio piece for this kind of work

Perfect. Let’s design **one serious project** that: - maps cleanly onto **agentic AI + RAG + automation** - is **non-chatbot**, system-oriented - looks impressive to *engineers*, not just recruiters - can be built incrementally but demoed convincingly - directly matches **multiple jobs in that feed** I’ll propose the project first, then break it into **architecture + milestones + tech choices**, so you can actually ship it. --- ## 🧠 Portfolio Project: ### **Spec → System Generator** **An Agentic RAG system that converts structured specs into production-ready application skeletons** > Think: “AI that understands engineering conventions, not vibes.” This hits: - Agentic AI - RAG over *code + specs* - Non-hallucinated generation - Tool-using agents - Validation & iteration loops --- ## 🚀 What the System Does (High-Level) **Input** - A Markdown spec (or folder of specs) - Optional constraints: - framework (FastAPI / Django / Next.js) - DB (Postgres / SQLite) - auth style - deployment target **Output** - A **real repo structure**, not a blob: - folders - config files - stubbed services - migrations - README - Output is: - convention-aligned - validated - reproducible **Key Point:** This is **codegen as systems engineering**, not “write me an app”. --- ## 🧩 Core Architecture (This Is the Selling Point) ### 1. **RAG Layer (Grounding)** Ingest: - Existing repos (your own or open-source) - Internal “gold standard” examples - Architectural patterns Index: - Code chunks (AST-aware if possible) - Specs - README + docs Purpose: > Teach the system *how you build things*, not what Python is. --- ### 2. **Agentic Workflow (Multiple Roles)** #### 🧱 Agent 1: **Spec Interpreter** - Converts Markdown → structured JSON - Extracts: - entities - APIs - data models - constraints - Rejects ambiguous specs #### 🧠 Agent 2: **Architect** - Decides: - directory layout - service boundaries - frameworks - Uses retrieved examples as anchors #### 🛠 Agent 3: **Generator** - Writes files one-by-one - Each file: - retrieved context - schema-validated output - no freeform dumping #### 🔍 Agent 4: **Validator** - Checks: - missing files - broken imports - naming consistency - spec coverage - Can send feedback *back* to Generator This is what people mean when they say **“agentic”** — not AutoGPT chaos. --- ## 🧪 Validation Layer (This Is Rare — and Valuable) You add **hard gates**: - JSON schemas for: - project manifest - file map - Static checks: - does every endpoint have a handler? - do models appear in migrations? - Optional: - run `pytest --collect-only` - run `ruff` / `black` > This alone separates you from 90% of “AI devs”. --- ## 🛠 Tech Stack (Pragmatic, Resume-Safe) **Core** - Python - FastAPI (API + orchestration) - Pydantic (schemas everywhere) **RAG** - FAISS or Chroma (local-friendly) - SentenceTransformers or OpenAI embeddings - Optional AST parsing via `tree-sitter` **Agent Orchestration** - Plain Python state machine (impressive) - OR LangGraph (if you want name recognition) **LLM** - OpenAI / Claude abstraction - Local LLM compatibility (Ollama) **Interface** - CLI (`specgen generate spec.md`) - Optional minimal web UI --- ## 📦 What You’ll Show in the Repo ### 1. **Clean README** - Problem statement - Architecture diagram - Why this is not a chatbot - Failure modes & mitigations ### 2. **Example Specs** - Simple CRUD - Complex multi-service spec ### 3. **Generated Repos (checked in)** - Show diffs - Show reproducibility ### 4. **Evaluation Notes** - Where hallucinations happen - How retrieval fixes them - What validation catches This is *portfolio gold*. --- ## 🧭 Build Plan (4–6 Weeks, Realistic) ### Week 1 - Spec → JSON interpreter - Simple repo skeleton ### Week 2 - RAG over example repos - Architectural agent ### Week 3 - File-by-file generation - Schema enforcement ### Week 4 - Validation + repair loop ### Optional Week 5–6 - Local LLM support - CLI polish - Blog post / demo video --- ## 🎯 Why This Wins Jobs This single project lets you credibly apply to: - **Agentic AI Engineer** - **RAG Consultant** - **AI Systems Architect** - **Codegen / Dev Tools roles** - **Founding engineer roles** And you can say, truthfully: > “I build AI systems that understand *engineering structure*, not just language.” --- ## Next Step (Your Choice) I can: 1. Write the **initial repo skeleton** 2. Design the **Pydantic schemas** 3. Draft the **agent state machine** 4. Help you position this for Upwork proposals 👉 Tell me which you want first, and we’ll start coding.

instead write a CLIne prompt to code this

Excellent choice. This is *exactly* the right use of **Cline**: one long, opinionated, system-level prompt that forces it to behave like a senior engineer, not a code spitter. Below is a **single, drop-in Cline prompt** you can paste directly into VS Code. It is designed to: - build the **Spec → System Generator** end-to-end - enforce **agentic structure, schemas, and validation** - avoid chatbot behavior - produce a **portfolio-grade repo**, not fragments This assumes: - Python - local-LLM compatible - no SaaS dependencies required --- ## 🧠 CLINE MASTER PROMPT **Project: Agentic Spec → Application Generator** ``` You are a senior software engineer and systems architect. Your task is to design and implement a production-quality Python project that converts structured Markdown specifications into validated application skeletons using an agentic RAG-based workflow. This is NOT a chatbot. This is a deterministic engineering system with validation gates. -------------------------------- CORE OBJECTIVE -------------------------------- Build a CLI-driven system that: 1. Accepts a Markdown specification describing an application 2. Interprets it into a strict structured representation 3. Uses retrieval over prior code examples to ground decisions 4. Generates a real repository structure (folders + files) 5. Validates completeness and consistency 6. Iterates until all validation passes -------------------------------- NON-GOALS -------------------------------- - Do NOT generate conversational interfaces - Do NOT dump entire repos as a single LLM response - Do NOT rely on prompt magic instead of schemas - Do NOT hallucinate frameworks or files not supported by retrieval -------------------------------- PROJECT STRUCTURE -------------------------------- Create a repository with this structure: specgen/ cli.py # CLI entrypoint config.py # global settings llm/ base.py # model abstraction rag/ ingest.py # code/spec ingestion index.py # vector store retrieve.py agents/ spec_interpreter.py # markdown -> structured spec architect.py # repo layout decisions generator.py # file-by-file code generation validator.py # consistency & completeness checks schemas/ spec.py # Pydantic models for interpreted specs project.py # file map + repo manifest utils/ fs.py markdown.py examples/ specs/ reference_repos/ README.md -------------------------------- AGENTIC FLOW (MANDATORY) -------------------------------- Implement the following pipeline explicitly: 1. SpecInterpreterAgent - Input: Markdown spec - Output: Structured Spec (Pydantic) - Reject ambiguous or missing requirements 2. ArchitectAgent - Input: Structured Spec - Uses retrieval over reference repos - Output: ProjectManifest (folders + files) 3. GeneratorAgent - Generates files ONE AT A TIME - Each file: - grounded by retrieved context - validated against schema - No freeform dumping 4. ValidatorAgent - Ensures: - all spec requirements are covered - imports resolve - filenames match manifest - Returns structured error reports 5. Repair Loop - Generator receives validator feedback - Regenerates only failing components -------------------------------- RAG REQUIREMENTS -------------------------------- - Implement a local vector store (FAISS or Chroma) - Ingest: - example repositories - prior generated projects - architectural templates - Retrieval must be used for: - folder structure decisions - framework conventions - boilerplate grounding -------------------------------- SCHEMA & VALIDATION RULES -------------------------------- - All intermediate representations MUST be Pydantic models - ProjectManifest must explicitly list: - directories - files - file purposes - Code generation output must conform to manifest - Fail fast on schema violations -------------------------------- CLI REQUIREMENTS -------------------------------- Implement a CLI: specgen generate spec.md --out ./output --framework fastapi Commands: - ingest - generate - validate -------------------------------- QUALITY BAR -------------------------------- - Deterministic, testable behavior - Clear separation of responsibilities - Minimal magic - Extensive docstrings explaining WHY decisions are made - README explaining: - architecture - agent roles - failure modes -------------------------------- IMPLEMENTATION STRATEGY -------------------------------- Proceed in this order: 1. Define schemas 2. Build SpecInterpreterAgent 3. Build ArchitectAgent 4. Implement RAG ingestion + retrieval 5. Build GeneratorAgent 6. Build ValidatorAgent 7. Wire CLI 8. Write README Do NOT skip steps. Do NOT compress stages. Do NOT optimize prematurely. -------------------------------- OUTPUT EXPECTATION -------------------------------- You are allowed to create and modify files freely. Before writing any code: - Summarize the system architecture - List agent responsibilities - Identify likely failure cases Then begin implementation incrementally, committing logical units of work. Think like an engineer, not a chatbot. ``` --- ## 🧩 Why This Prompt Works (Important) This prompt: - **forces decomposition** - blocks “one-shot repo dumps” - compels **schemas-first thinking** - makes validation unavoidable - reads like an internal engineering RFC Cline + a decent local coder model will *behave* under this. --- ## Next Optional Upgrades If you want, next we can: - Add **explicit unit test generation** - Add **AST-level validation** - Add **Playwright automation** (spec → UI scaffold) - Tune this prompt for **Qwen / DeepSeek / CodeLlama** Just tell me the next move.