Hire LLM engineers
LLM engineering is the narrow, deep version of AI engineering: everything downstream of the model call. Context assembly, retrieval, structured output, tool use, guardrails, evaluation, and the cost curve that decides whether the feature ships or dies in review.
Free to search, every filter open · opens filtered to AI Application Integration.
16 scored profiles match — here are the top 6
How to tell a real one from a résumé that says the words
- Talks about context windows as a budget, not a spec sheet number.
- Has an opinion on structured output and function calling that comes from a bug, not a blog post.
- Measures retrieval separately from generation. Teams that don't, debug the wrong half for weeks.
- Has moved a workload between providers, or has a real reason they haven't.
How to hire LLM engineers with this database
- Start from the capability filters rather than job titles — LLM work sits under half a dozen different ones.
- Screen for the specific stack you run: pgvector versus a managed vector store, agents versus a single well-built call.
- Ask each shortlist candidate for the evaluation they are proudest of. The answer separates the field faster than any other question.
Every filter is free and each search shows the top 10 matches with the true total. The full ranked list is a single one-time payment — see pricing.
Questions about hiring LLM engineers
Is an LLM engineer the same as an AI engineer?
Nearly. LLM engineer is the narrower title — someone whose work is specifically about large language models rather than the wider category of AI features. In practice most job specs use the terms interchangeably, so search both.
What should an LLM engineer be able to show me?
A retrieval or agent system in production, an evaluation set they built for it, and numbers for quality, latency and cost before and after a change they made. Anyone who has done the job has all three.
Do LLM engineers need a machine learning background?
Not usually. The strongest people in this niche often come from backend or product engineering — the job is systems work with a probabilistic component. A research background helps for fine-tuning and evaluation design, and matters less elsewhere.
Hiring for a different AI role?
Or search by the skill you need
Everyone in here is scored 0–100 on what they have demonstrably shipped, with the reason behind every number.
Search the database →Scores reflect public professional signals only, and anyone can ask to be removed. Hiring decisions stay yours — this is a starting point, not a verdict on anyone's ability. Being hired for AI work yourself? Score your own profile.