Hire RAG engineers to improve retrieval quality on your data.

A RAG engineer works on how your system finds and supplies the information a model needs. The scope can include document ingestion, chunking, indexing, query handling, ranking, access permissions and retrieval evaluation. The job becomes valuable when answer quality depends on source material that is difficult to find, keep current or present with the right context.

Start with the failure you need to solve: a correct document is missing from results; the result contains the wrong passage; an answer ignores good evidence; or restricted content reaches the wrong user. These require different interventions. A strong candidate separates retrieval failures from generation failures before recommending a new database or model.

Keyword and vector search can be combined, and ranking can refine the returned context. The useful hiring signal is whether the engineer can evaluate those choices on your queries while accounting for latency and access controls. Microsoft’s RAG architecture guidance covers these engineering considerations.

Bring to the intake: representative questions, document types and languages, corpus update frequency, permission boundaries, existing search components and examples of poor answers. If the main need is application delivery around a simple retrieval feature, an LLM engineer may cover it. A dedicated retrieval role is easier to scope when the quality problem is explicit.

We look across search, information retrieval, data engineering and applied AI backgrounds. Targeted outreach, referrals and technical communities help us find engineers whose work matches your corpus and product. A RAG title, publication record or open-source profile is not a prerequisite.

The role-specific interview follows a retrieval decision end to end. We ask how the candidate built a query set, established relevance labels, chose a baseline and diagnosed a failure. Useful questions include: “What would you inspect when good source material never reaches the answer?” and “How would you test whether a reranker is worth the extra latency?” A human recruiter reviews the AI-supported interview and the evidence before delivery.

Candidates can use a redacted project walkthrough, a relevant work sample or public technical work. We assess the relevance of their experience rather than imposing a minimum document count or query volume. Your shortlist explains their contribution, demonstrated strengths and the retrieval questions your team should explore next.