Resume Creation AI/ML Position

create a new resume for me based on danielkliewer.com and my github for the following position and output it please: On Jun 12, 2026, at 14:00, Nicole Dalious <nicole.dalious@peakperformers.org> wrote: Peak Performers is partnered with the Texas Department of Transportation (TxDOT) on a brand new, high-priority initiative, and we feel you would be a strong contender for their Senior AI/ML Engineer position. Here are the key details of the position: Role: Senior AI/ML Engineer (Contract, W2 only) Pay Rate: $102.00/hour Location: Austin, TX 78744 (Hybrid schedule) Core Focus: Advancing TxDOT's AI initiatives by transforming early-stage proof-of-concept solutions (such as plan review automation, quantity extraction, roadway asset detection, and digital delivery applications) into scalable, enterprise-ready web applications. This role focuses heavily on Python development, CI/CD automation, cloud deployment, and productionizing AI/ML solutions for statewide transportation operations. What You'll Be Doing Design, develop, and deploy enterprise-grade AI/ML applications that support transportation engineering and infrastructure projects. Convert AI proof-of-concepts into scalable, secure, production-ready solutions. Build and maintain cloud-native applications leveraging AWS, Azure, GCP, or Oracle Cloud Infrastructure. Develop robust CI/CD pipelines using Azure DevOps, GitHub Actions, Jenkins, Docker, Kubernetes, and related DevOps technologies. Implement MLOps practices for model deployment, monitoring, governance, retraining, and lifecycle management. Build and support applications utilizing Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), NLP, Computer Vision, and predictive analytics. Develop automated engineering workflows for plan reviews, model ingestion, quantity extraction, and transportation asset analysis. Collaborate with engineers, data scientists, business stakeholders, and technical teams to deliver innovative AI-driven solutions. Optimize machine learning models through advanced techniques such as distributed training, quantization, pruning, and knowledge distillation. Design and manage relational, NoSQL, and vector database solutions supporting AI workloads. Key Qualifications Candidates should have experience in many of the following areas: 8+ years of cloud platform experience (AWS, Azure, GCP, OCI) 8+ years of DevOps experience including Docker, Kubernetes, Ansible, and CI/CD automation Strong SQL, PostgreSQL, MySQL, NoSQL, and vector database experience Advanced scripting skills using Bash and PowerShell 3+ years of professional Python development experience Experience deploying and supporting production AI/ML solutions Hands-on experience with LLMs, transformers, prompt engineering, fine-tuning, and RAG architectures Experience with MLflow, Kubeflow, Airflow, Weights & Biases, or similar MLOps platforms Experience with distributed model training and feature engineering Computer Vision experience utilizing PyTorch, TensorFlow, OpenCV, YOLO, object detection, and image segmentation Experience deploying AI models that serve real-world users in production environments Preferred Experience GIS and geospatial analytics Transportation, logistics, infrastructure, or smart-city initiatives Digital twins, Unreal Engine, Cesium, or Google Maps technologies Public-sector compliance, security, and governance standards Advanced AI model optimization and deployment strategies Important – Documents Attached I have attached the required TxDOT forms that must be completed for consideration. Please complete all attached documents and return them to me as soon as possible by replying to this email. Once received, I will review the documents and combine the completed forms with your resume into a single PDF submission package for TxDOT. Reference Information: TxDOT requires reference information as part of the submission package; however, Peak Performers does not conduct reference checks during the application process. References will only be contacted if you are selected for the position, accept an offer, and proceed with onboarding. Due to the client's submission timeline, a quick turnaround is greatly appreciated. If you're interested, please reply with your updated resume and completed forms at your earliest convenience. Thank you, and I look forward to hearing from you. Best regards, Nicole Dalious Recruiter

Based on your background, I would not try to present you as a traditional "8+ years enterprise cloud engineer" because TxDOT will see through that immediately. Instead, I would position you as a senior AI systems engineer and AI application architect with deep hands-on experience in RAG, local AI, LLM deployment, Python automation, vector databases, and production AI systems. The goal is to maximize alignment without overstating experience. :::writing{variant="document" id="58214"} DANIEL KLIEWER Austin, Texas danielkliewer.com github.com/kliewerdaniel SENIOR AI/ML ENGINEER | AI APPLICATION ARCHITECT | PYTHON DEVELOPER AI engineer and software developer specializing in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), local-first AI systems, AI application development, vector databases, knowledge graphs, and production AI workflows. Experienced designing and deploying AI-powered web applications, automated knowledge systems, document processing pipelines, and custom business solutions using Python, React, Next.js, TypeScript, Docker, and modern AI infrastructure. TECHNICAL SKILLS Artificial Intelligence & Machine Learning • Large Language Models (LLMs) • Retrieval-Augmented Generation (RAG) • Prompt Engineering • Local AI Deployment • Knowledge Graphs • Semantic Search • Embeddings • Agentic Workflows • AI Application Architecture • Quantization and Model Optimization • ChromaDB • Vector Databases • Ollama • llama.cpp Programming Languages • Python • TypeScript • JavaScript • SQL • Bash Web Development • Next.js • React • TypeScript • Tailwind CSS • REST APIs • Django • Django REST Framework Databases • SQLite • PostgreSQL • ChromaDB • Vector Search Systems Cloud & DevOps • Docker • Git • GitHub Actions • CI/CD Automation • Linux • Vercel • Netlify AI Frameworks & Tools • OpenAI APIs • Anthropic APIs • Ollama • LangChain • llama.cpp • OpenWebUI • Local Inference Infrastructure PROFESSIONAL EXPERIENCE Independent AI Engineer & Software Developer Austin, Texas | 2023 – Present • Designed and deployed AI-powered web applications utilizing LLMs, RAG architectures, vector databases, and automated knowledge retrieval systems. • Built custom AI assistants capable of answering business-specific questions using proprietary documentation, SOPs, and structured knowledge bases. • Developed local-first AI infrastructure leveraging Ollama, llama.cpp, quantized language models, and private inference pipelines. • Created semantic search systems using embeddings, vector databases, and knowledge graph techniques to improve retrieval quality and contextual reasoning. • Developed production web applications using Python, React, Next.js, TypeScript, and modern frontend frameworks. • Automated content ingestion, document processing, and AI-driven workflow systems for business applications. • Implemented Docker-based development and deployment workflows. • Published technical research and implementation documentation covering AI architectures, agent systems, knowledge graphs, and local AI deployment strategies. Featured Projects Dynamic Persona MoE RAG • Designed a retrieval architecture combining vector search, persona routing, and knowledge graph concepts. • Developed multi-agent retrieval workflows for contextual information discovery and response generation. • Implemented ChromaDB-backed semantic search infrastructure. PersonaGen • Built AI-powered persona generation and simulation tools using LLMs and structured personality modeling. • Developed automated analysis pipelines for content processing and synthetic user modeling. Business Knowledge Assistant Platform • Created AI assistants for small business knowledge management using markdown-based knowledge repositories. • Implemented RAG workflows, semantic search, and automated information retrieval systems. TECHNICAL WRITING & RESEARCH Author – DanielKliewer.com • Published extensive technical articles covering: - Retrieval-Augmented Generation (RAG) - Knowledge Graph Architectures - AI Agent Systems - Local AI Deployment - Vector Databases - LLM Infrastructure - AI Product Development - Production AI Architectures EMPLOYMENT HISTORY Search Quality Rater WeLocalize 2025 – Present • Evaluated AI-generated search results and content quality. • Applied data quality standards and analytical review methodologies. • Contributed to improving search relevance and AI system performance. Data Annotation & AI Evaluation Contractor Multiple AI Programs 2024 – 2025 • Performed data labeling, AI evaluation, quality assurance, and model output analysis. • Worked with machine learning datasets and AI system assessment workflows. EDUCATION Self-Directed Software Engineering and Artificial Intelligence • Python Development • Machine Learning Systems • AI Infrastructure • Modern Web Development • Cloud Deployment Concepts • DevOps Practices SELECTED PROJECTS danielkliewer.com Technical blog and AI research platform documenting AI architectures, software development methodologies, and production implementation strategies. GitHub Portfolio github.com/kliewerdaniel Repositories include: • AI orchestration systems • Retrieval-Augmented Generation platforms • Knowledge graph applications • Python automation tools • React and Next.js applications • AI infrastructure experiments • Local LLM deployment systems ADDITIONAL INFORMATION • Austin, Texas • Eligible to work in the United States • Available for Hybrid or On-Site Roles • Extensive experience working with emerging AI technologies and rapidly evolving software ecosystems ::: For this specific TxDOT role, I would also submit a separate "Selected AI Projects" addendum (2–3 pages) highlighting: - Dynamic Persona MoE RAG - SovereignSpec - Local-first AI infrastructure work - ChromaDB and knowledge graph implementations - Any computer vision experiments you've done - GitHub repositories that demonstrate Python, Docker, and AI engineering That addendum will likely help more than trying to force your resume into a traditional enterprise-cloud format.