Hire RAG engineers
Retrieval is where most AI products actually live or die. The model is a commodity; what you feed it is not. RAG engineering is unglamorous information-retrieval work — chunking, embeddings, ranking, freshness — dressed in new vocabulary.
Free to search, every filter open · opens filtered to RAG.
Scored profiles matching RAG engineers
How to tell a real one from a résumé that says the words
- Evaluates retrieval on its own — recall on a labelled set — before blaming the model.
- Has opinions about chunking that came from failures, not from a tutorial.
- Has tried hybrid search or reranking, and can say when plain vector search was enough.
- Thinks about freshness and permissions, the two things that turn a good demo into a support incident.
How to hire RAG engineers with this database
- Search the RAG tool tag, then widen to the vector stores and frameworks you run.
- Prioritise anyone whose evidence mentions evaluation alongside retrieval — that pairing is rarer than it should be.
- Interview on a corpus like yours: messy, permissioned, and changing daily.
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 RAG engineers
What does a RAG engineer do?
They build the retrieval layer that grounds a model in your data: ingestion, chunking, embedding, indexing, ranking and the evaluation that proves the right context is being found.
Is RAG still relevant with long context windows?
Yes — for cost, latency, freshness and permissions. Long context makes naive retrieval unnecessary for small corpora and changes nothing about a large, changing, access-controlled one.
How do I test a RAG candidate?
Give them a realistic failure: users say answers are wrong. Ask how they'd find out whether it's retrieval or generation. A strong candidate designs that experiment in under a minute.
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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.