Hire AI Developers Who Have Shipped

The AI talent market has a signal problem: every resume now says "LLM experience," and most of it means API tutorials, not production systems. The engineers worth hiring are the ones who can talk concretely about retrieval evaluation, hallucination containment, token cost budgets, and what broke at scale — because they've been on call for it. That's what our vetting selects for. Every AI engineer we place has passed a live build session (a working RAG or agent feature, built in front of us) and can point to LLM features running in production.

Hire an engineer, or hand us the build?

If you have an engineering team and a roadmap, hire AI developers into it — they'll transfer capability to your team as they build. If you have an AI feature or product in mind but no team to absorb it, our AI agent development and LLM integration practices deliver it as a scoped project instead — and our own portfolio of 12 in-house AI agents shows the pattern quality you can expect. Between the two sits the dedicated AI pod: a self-managed team for a long-running AI roadmap.

See our KnowledgeCore case study for the kind of production RAG engineering our placed AI developers are vetted against — 12,000+ ingested documents, role-based access, 1.4s p95 latency.

Standard technical interviews do not discriminate here. Algorithm questions test something unrelated to the job, and "have you used LangChain" gets a yes from everyone. The questions that work are the ones where a tutorial answer and a production answer sound completely different.

Ask how they evaluated a retrieval system

Anyone can build a RAG pipeline that returns something. The production question is how they knew it was returning the right thing. A real answer involves a labelled evaluation set, retrieval metrics measured before generation was ever considered, and a specific failure they found and fixed — chunk boundaries splitting a table, an embedding model that could not distinguish two product names, reranking that helped or did not. A tutorial answer describes the architecture and stops.

Ask what their feature cost to run

Engineers who have shipped LLM features to real traffic know their per-request token cost, because someone made them find out. They can describe the lever they pulled — caching, a smaller model for the easy path, trimming context that was not earning its place. Engineers who have not shipped have never been asked.

Ask about a hallucination that reached a user

This is the most reliable single question. Everyone who has run an LLM feature in production has one. The answer reveals whether they think about containment structurally — grounding, citation, confidence thresholds, human review on the risky path — or whether they will tell you prompt engineering solved it.

Have them build, not whiteboard

A 90-minute live session building a small working retrieval or agent feature tells you more than any interview. You see how they handle an API that misbehaves, whether they check outputs or assume them, and how they reason when the first approach does not work. This is what our own assessment stage is, and it is worth running even on candidates you did not source through an agency.

The red flags

A portfolio consisting entirely of demos with no users. Fluency about models paired with vagueness about evaluation. No opinion on when not to use an LLM — engineers who have shipped have usually removed one. And an inability to explain a tradeoff they made, which generally means they did not make it.