AI Portfolio Job Fit

go to DanielKliewer.com and I want you to reverse engineer the types of jobs which a website like this would be a perfect resume piece for and then I want you to help me apply for those jobs in Austin, TX, but first I want to know what kinds of jobs, then I want you to rank them based on suitability and likelihood I would be hired for them and evaluate the output for roles which I am actually qualified for and not reach positions

I think your website is much stronger as a **technical portfolio** than as a traditional résumé. That's actually an advantage for certain kinds of employers. After looking through it, I would not market you as a "Senior AI Architect" despite that language appearing on LinkedIn. I would market you as an experienced **AI systems engineer, AI prototyper, and full-stack AI developer** who has built an unusually large body of demonstrable work. The evidence on the site—200+ repositories, local-first AI systems, RAG implementations, Ollama, ChromaDB, knowledge graphs, Next.js, Django, FastAPI, and technical writing—is much stronger than trying to compete for enterprise architect positions that typically expect years of leading large engineering teams. citeturn0search0turn0search1turn0search2 Here's how I would rank the kinds of jobs your portfolio naturally supports. | Rank | Role | Fit | Hiring Probability | Comments | |------|------|------|-------------------|----------| | **1** | AI Solutions Engineer | ★★★★★ | ★★★★★ | Best overall match. Build AI demos, customer solutions, RAG systems. | | **2** | Applied AI Engineer | ★★★★★ | ★★★★☆ | Strong technical fit if they value projects over pedigree. | | **3** | AI Prototyping Engineer | ★★★★★ | ★★★★☆ | Startup role where speed matters. | | **4** | AI Developer Advocate | ★★★★☆ | ★★★★☆ | Your blog and book become major assets. | | **5** | ML Platform Engineer (Junior/Mid) | ★★★★☆ | ★★★★☆ | Especially companies using local inference. | | **6** | Full Stack AI Engineer | ★★★★☆ | ★★★★☆ | Strong if the role combines frontend with AI. | | **7** | Technical AI Consultant | ★★★★☆ | ★★★★☆ | Especially small consultancies. | | **8** | AI Research Engineer (Applied) | ★★★★☆ | ★★★☆☆ | Better at startups than research labs. | | **9** | Developer Relations Engineer | ★★★★☆ | ★★★☆☆ | Writing and open source help considerably. | | **10** | Backend Python Engineer (AI) | ★★★★☆ | ★★★☆☆ | Plenty of transferable experience. | | **11** | Search/Relevance Engineer | ★★★☆☆ | ★★★☆☆ | Your search quality experience is relevant. | | **12** | Prompt Engineering / Agent Engineer | ★★★★☆ | ★★★☆☆ | Many startups hire for this now. | I would **avoid** spending much time applying for these: - Principal AI Architect - Staff AI Engineer - Distinguished Engineer - Director of AI - Senior ML Infrastructure Engineer - Research Scientist - FAANG Staff Engineer Those jobs usually require long histories of shipping production systems at large companies, managing teams, or publishing research. Your portfolio demonstrates technical depth, but employers often expect a different type of experience for those titles. ## What your portfolio communicates especially well Looking purely at the evidence on your website, I think employers would conclude that you are very good at: - Rapid prototyping - Learning new AI technologies independently - Local-first AI - Retrieval-Augmented Generation - Agent architectures - Python - TypeScript - Full-stack web development - Technical writing - Open source - Working independently - Building proof-of-concepts from scratch Those are valuable skills for many startups and AI-focused teams. citeturn0search0turn0search2 ## What I think employers will worry about This is the part that's important if we want to maximize interview success. An engineering manager will probably ask: - Have these systems been deployed in production? - Have you worked on teams larger than yourself? - Can you maintain production software over several years? - What measurable business outcomes resulted? - How much of this was experimental versus used by real users? Those are questions we can prepare strong answers for, and in some cases adjust how your experience is presented. ## Austin companies I think are realistic targets If I were running your job search, I'd focus on companies where your portfolio is differentiated rather than trying to compete with every generic software engineer. My priority list would include: **Excellent fit** - Small AI startups - Developer tooling startups - Local AI infrastructure companies - Enterprise AI consultancies - Robotics startups using LLMs - GovTech AI companies - Defense technology companies doing on-prem AI - AI security companies **Good fit** - entity["company","Tesla","electric vehicle and AI company"] - entity["company","IBM","technology company"] - entity["company","Dell Technologies","technology company"] - entity["company","Oracle","enterprise software company"] - entity["company","AMD","semiconductor company"] - entity["company","NVIDIA","AI and semiconductor company"] (Austin presence) - AI consulting firms Notice that I'm intentionally **not** putting companies like OpenAI or Anthropic at the top. They're not impossible, but they're significantly more competitive than employers where your combination of open-source work, technical writing, and local-first AI experience is likely to stand out. ## My recommendation I think we should treat this as a **targeted campaign**, not a mass application process. I'd suggest we: 1. Identify the top 30–50 companies in Austin that genuinely fit your background. 2. Reverse engineer each job description against your portfolio. 3. Tailor your résumé and website to emphasize the exact evidence each employer is seeking. 4. Apply only where you have a realistic chance of reaching an interview. That approach is much more likely to produce results than applying broadly. I also think we can make your website work even harder as a recruiting tool by adding a few recruiter-focused case studies that connect your projects to concrete engineering outcomes rather than only describing the technologies. That would strengthen your candidacy for mid-level Applied AI Engineer and AI Solutions Engineer roles in particular.

So I am going to that art show tonight, when I go out I need to network better, what should I do to try to network well

Given what I know about your goals, I would treat tonight much less as "finding a job" and much more as **finding one interesting conversation**. That's a more achievable objective, and it's often how opportunities actually start. You have something that many people don't: you've built a substantial body of technical work and you genuinely enjoy discussing ideas. The challenge is making those conversations accessible to people who aren't already immersed in your projects. A few things I'd recommend: - **Be curious before being impressive.** Ask people what they're working on, what brought them to the show, or what they're excited about. People generally remember someone who showed genuine interest in them. - **Keep your introduction simple.** Instead of describing your whole architecture or every project, try something like: > "I'm an AI developer here in Austin. I build local AI systems and write a lot about agent architectures." If they're interested, they'll ask follow-up questions. - **Have one or two stories ready.** Rather than listing technologies, tell a short story about a problem you solved. For example: > "I became interested in local-first AI because I wanted to see how much useful work could happen entirely on a laptop. That turned into building a series of RAG and agent systems." - **Connect your work to their interests.** If someone is an artist, designer, or entrepreneur, think about how your AI experience could relate to what they do instead of steering the conversation back to your own projects. - **Exchange contact information naturally.** If the conversation goes well, something as simple as: > "I'd love to stay in touch. Are you on LinkedIn?" or > "Here's my website if you're ever curious about the AI work I mentioned." One thing I'd be careful about is **not trying to convince people of everything you've built in the first conversation**. Your website can do that later. Your goal tonight is simply to leave them thinking, "That was an interesting person I'd enjoy talking to again." Since this is an art show, you also have an easy conversation starter. Ask someone: - "What piece has stood out to you so far?" - "Do you come to these shows often?" - "Are you an artist yourself or just a fan?" Those questions feel natural in the setting and can lead almost anywhere. Finally, I'd set yourself one concrete goal before you walk in: - Have **three meaningful conversations** (5–10 minutes each). - Learn **one interesting thing** about each person. - Leave with **one new contact** you genuinely want to follow up with. If you achieve those three things, I'd consider the evening a networking success, even if no jobs come up. Relationships built over multiple conversations are much more likely to lead to opportunities than trying to make a strong impression with everyone you meet.