Hiring AI engineers is where a lot of budget gets wasted, on the wrong role, a mis-scoped spec, or a generalist learning ML on your dime. Here's a playbook to get it right and move fast.
Define the real role
Many 'we need an AI engineer' asks are actually two roles: someone who owns the model (data, evaluation, path to production) and someone who integrates it into your product and systems. Naming that split up front prevents expensive mis-hires.
What to assess
- Modeling: data handling, evaluation design, knowing when not to use ML.
- Production: serving, latency, monitoring, cost, the MLOps reality.
- Judgment: framing the business problem, not just the technical one.
- Collaboration: working with product and domain experts.
A process that respects the bar
- 1Write the capability gap and success metric before the job description.
- 2Screen with a short, realistic work sample tied to your actual problem.
- 3Run a pairing session, watch how they think, not just what they know.
- 4Check for oversight skills with AI tools, a new must-have.
- 5Decide fast once the signal is clear, top candidates don't wait.
