Hire LLM Engineers

Retrieval is where most LLM products succeed or fail, so probe RAG depth first. A capable engineer talks about chunking strategy, hybrid keyword-plus-vector search, re-ranking, metadata filters for permissions, and how to tell a retrieval miss from a generation error. They should be able to explain when a long context window removes the need for retrieval and when it simply raises cost.

Evaluation is the second filter. Look for someone who builds a labelled test set from real user questions, scores answers for groundedness and correctness, uses model-graded checks carefully with human spot checks, and runs the suite on every prompt or model change - so quality regressions are caught before release rather than reported by users.

Then check prompt and tool design, cost and safety. Good engineers write structured prompts with clear instructions and examples, define tools and function schemas the model can call reliably, and validate structured output. They reason about cost and latency trade-offs - smaller models for routing and extraction, larger ones for hard reasoning, prompt caching, streaming and batching - and they build guardrails: input filtering for prompt injection, output checks for leaked data or unsupported claims, and safe fallbacks when the model is unsure.

Early work is usually discovery and a baseline: collecting representative questions or tasks, auditing the data sources the system will draw on, and building a first evaluation set so there is a number to improve. Many teams find their first prototype was judged on a handful of demo questions; turning that into a measurable baseline is often the most valuable early contribution.

From there, responsibilities typically move to improving retrieval and prompts against that baseline, adding observability - logging prompts, retrieved context, token usage and latency per request - and putting cost controls and guardrails in place before wider rollout. By the end of the first quarter an LLM engineer should own the release process for model and prompt changes, with evaluations acting as the gate.

Project-based delivery suits a well-defined outcome: a proof of concept that answers whether RAG over your documents is accurate enough, an evaluation harness for an existing feature, or a migration from one model provider to another. A dedicated engineer suits an ongoing product where models, prompts and data change continuously and someone must keep quality measured over time.

A practical path is to start with a bounded project that produces an evaluation baseline and a go or no-go decision, then continue with dedicated capacity if the feature ships. Pricing is transparent and milestone-based, the client owns the code, prompts, evaluation data and IP, and an NDA is signed before kickoff - important when proprietary documents feed the system.

Give candidates a realistic, small task rather than trivia: a few dozen documents, a set of questions with known answers, and a request to build a minimal RAG pipeline and report its accuracy. What matters is less the score than how they measure it, how they diagnose failures and what they would change next.

Add a design discussion on one of your real use cases. Ask how they would stop a support assistant from inventing refund policies, how they would keep per-request cost in check as usage grows, how they would handle documents a user is not permitted to see, and how they would decide between a hosted model and a self-hosted open model. Clear answers about trade-offs, evaluation and failure modes are the strongest signal of seniority.