If you want to hire LLM developers who will ship something real, test for four things before you sign: whether they can build retrieval that returns the right context, whether they write evals before they tune prompts, whether they control cost and latency, and whether they can make a model call tools safely. A candidate who aces a LeetCode round and has a chatbot demo on GitHub may have none of the four.
Demand makes this harder. Lightcast's summary of the 2026 Stanford AI Index says 2.5% of all US job postings now mention AI skills, and AI Engineer tops LinkedIn's 2026 Jobs on the Rise list in the US, with LangChain, RAG and PyTorch as the most common skills. Plenty of resumes now say "LLM." Far fewer people have run one in production.
Key Takeaways
- Hire for production LLM work: retrieval quality, evals, cost and latency control, and safe tool use.
- Ask for one paid or take-home task that mirrors your real use case and includes an eval set, then review it together.
- A good LLM developer starts with prompts and evals and reaches for RAG or fine-tuning only when the evals show why.
- Most "LLM developer" roles at startups are product engineering roles with an LLM inside, so test general backend skill too.
What an LLM developer actually does
At a startup, the job is rarely training models. It's building features on top of hosted models: search over your documents, an assistant inside your product, a pipeline that extracts fields from contracts, an agent that takes actions through your API.
That work has a shape. You define what "good" looks like, build a test set, try the simplest approach, measure, and only then add complexity. OpenAI's guidance says "prompt engineering is typically the best place to start," with "a good prompt with an evaluation set of questions and ground truth answers." It recommends retrieval (RAG) when the model lacks knowledge and fine-tuning when behavior is the problem, such as inconsistent formatting or tone. A developer who jumps to fine-tuning on day one usually hasn't measured anything.
Evals are the line between hobby and production. OpenAI's evaluation best practices note that "models sometimes produce different output from the same input," which "makes traditional software testing methods insufficient." Anthropic's docs say evals should "mirror your real-world task distribution" and prefer volume: "more questions with slightly lower signal automated grading is better than fewer questions with high-quality human hand-graded evals."
If a candidate can talk about their eval set the way a backend engineer talks about their test suite, keep talking to them.
What to test before you hire LLM developers
| Skill | A real test task | What good looks like |
|---|---|---|
| Retrieval (RAG) | Give 200 of your docs (or public ones) and 20 questions; build retrieval that answers them | Chunking chosen for the content, retrieval measured separately from generation, failures explained |
| Evals | Same task: write the eval set and grader first | Clear success criteria, automated grading, a score they can improve |
| Cost and latency | Ask them to cut cost per request in half without losing eval score | Caching, smaller model for easy cases, shorter context, numbers before and after |
| Tool use | Let the model call two functions in a toy API (look up an order, issue a credit) | Validated arguments, a confirmation step for anything that changes data, logged calls |
| Failure handling | Make the model API return errors or garbage 10% of the time | Retries with limits, fallbacks, a sensible message to the user |
Keep it to four to six hours of work, and pay for it if it runs longer than a normal take-home. Then spend an hour reviewing it with the candidate. The review shows more than the code: why they chunked that way, what the eval missed, what they'd do with another week.
Our guides on how to hire RAG developers, how to vet an engineer's AI skills and LLM engineer vs AI engineer go further on interview questions.
A Concrete Version
Say a 15-person legal-tech startup wants an assistant that answers questions about a customer's contracts, with citations.
The weak hire builds it in a week. It demos well. In the second month, customers find it cites clauses that don't exist, costs $0.40 a query because it stuffs whole contracts into context, and times out on 80-page agreements. Nobody can say whether a prompt change made things better or worse.
The strong hire spends the first week on a 150-question eval set drawn from real customer questions, with expected answers and cited clauses. Version one scores 61% on correct citations. They change chunking to follow contract sections, retrieve per section, and add a check that every cited clause actually appears in the retrieved text. The score climbs to the high 80s. They route short questions to a cheaper model and measure that the score holds. When the customer asks "how good is it?", there's an answer with a number.
Same model, same budget. The difference is the engineer's habits, and the test task above is designed to show those habits before you hire.
The Honest Counterpoint
You might not need an LLM specialist at all. If your feature is a single API call that summarizes text, a solid backend engineer can build it from the docs in a few days. Hiring a dedicated LLM developer for that is paying for skill you won't use.
You might need more than one. If you're training or fine-tuning models on your own data, you need ML engineering and data infrastructure skills that most LLM application developers don't have. See how to hire an AI engineer for that profile.
And demos lie in both directions. A rough-looking candidate project with a real eval set beats a polished UI with none.
Frequently Asked Questions
What skills should an LLM developer have?
Retrieval (RAG) design, writing and running evals, prompt design, cost and latency optimization, tool or function calling, and solid backend engineering. Production experience matters more than any single framework.
How do I test an LLM developer in an interview?
Give a short task that mirrors your use case, include a small dataset and ask them to write the eval first. Then review it together and ask what they'd change. That conversation shows judgment better than trivia questions about frameworks.
Should I hire a freelance LLM developer or a full-time one?
For a prototype or a two-week proof of concept, freelance can work. For a feature customers depend on, you want someone who stays to maintain the evals and handle model changes, which means full time or a long staff augmentation engagement.
The Bottom Line
Hire LLM developers on evidence: an eval set, a measured improvement, a cost cut with numbers. If you want to see vetted senior engineers, including AI engineers when we have the stack on the bench, see who's available or request a shortlist with your use case.
Roberto Espinoza is CEO of Ruzora, which helps US startups hire pre-vetted senior LATAM engineers, with a vetted shortlist in 72 hours. See available engineers.
