How to vet an AI engineer: a practical guide

Hiring an AI engineer is high-stakes: a general coding screen won't tell you whether someone can actually build and ship AI. This guide covers how to vet AI engineers properly — the same principles Talveda's own multi-stage process is built on.

1. Screen for AI depth, not just coding

Plenty of strong software engineers have thin AI experience. Assess the specific specialism you need — LLMs and RAG, computer vision, NLP, MLOps — with questions and tasks that reveal real, hands-on depth, not surface familiarity.

2. Use a hands-on, realistic assessment

Trivia and whiteboard puzzles predict little. A short, practical exercise close to your real problem — debugging a model, designing a pipeline, evaluating an approach — tells you far more about how someone actually works.

3. Probe system thinking

For senior and architect roles, assess trade-off reasoning: how they'd design for scale, cost, latency and reliability, and how they choose between build and buy. AI systems fail in production for architectural reasons as often as modelling ones.

4. Evaluate communication and collaboration

An embedded engineer has to explain their reasoning, take feedback and work in your rituals. Assess spoken and written communication and remote-collaboration habits — they make or break a distributed hire.

5. Check references and validate with a trial

Reference checks confirm track record; a scoped, paid trial confirms fit on your real work before you commit. The best way to de-risk an AI hire is to see the work before the engagement deepens.

How Talveda applies this

Because AI is all we do, every specialist passes a multi-stage screen — profile, technical deep-dive, communication, references and background — before they ever reach your shortlist. You still interview and decide; we just make sure you're choosing between genuinely qualified people. See our vetting process for detail.