Why a portfolio beats a résumé in AI
In a field this new, a list of credentials tells a hiring manager almost nothing — everyone claims to “use LLMs”. A portfolio of shipped projects is the only signal that cuts through, because it proves the one thing employers are actually buying: that you can take a model and turn it into something that works. This guide covers the three projects worth building, how to document them, and how to ship a live demo a recruiter can click in seconds.
The three projects that get you hired
Aim for breadth across the things an applied AI role actually requires.
1. An end-to-end LLM product. A RAG chatbot over a real document set, an agent that uses tools, or a “chat with your data” tool. This shows you can do prompt design, retrieval, evaluation, and a usable interface — the bread and butter of an AI engineer job.
2. A data or model pipeline. Something that ingests data, processes it, and produces a model or dataset on a schedule. This proves you understand pipelines, not just one-off notebooks, which is what separates engineers from tinkerers.
3. A polished single-purpose tool. A small, beautifully finished utility — a client-side summariser, a fine-tuned classifier behind an API. This shows you can finish and polish, the rarest signal of all.
How to document and demo them
Documentation is where most portfolios fail. Each project needs a README with a live demo link at the top, a screenshot or short GIF, a plain-English explanation of what it does and why, working setup steps, and a short architecture-and-tradeoffs note. Deploy every project somewhere a reviewer can try it — free tiers cover almost any demo, and fully client-side tools need no backend at all. Then collect all three on a simple personal site with one paragraph each and the demo links front and centre. The goal is that a hiring manager, in five minutes and three clicks, sees working software and concludes you can ship — because in this market, that is the entire hiring bar.