Define the Problem First: AI is a tool, not a strategy. Before you hire, be specific about what you’re actually trying to solve — what decision needs to be made, what process needs to be automated, what prediction needs to be accurate. Vague briefs produce expensive experiments with no clear outcome.

Look for Production Experience: Research experience and production experience are very different things. Ask specifically about systems they’ve deployed and maintained — not projects they worked on in a lab or academic setting.

Assess Domain Knowledge: An AI developer who understands your industry will outperform one who doesn’t, even if the latter has stronger raw technical skills. Healthcare AI, fintech AI, and e-commerce AI have different constraints, different data characteristics, and different failure modes.

Test Practical Judgment: The best AI developers know when not to use AI. If someone can only see ML solutions to every problem, that’s a red flag. Look for people who think about the simplest effective approach first.

Check Their Data Instincts: How someone talks about data tells you a lot. Strong AI developers are obsessed with data quality, deeply skeptical of biased training sets, and clear-eyed about what their models can and can’t do.

Verify MLOps Capability: Ask how they’ve handled model drift, retraining pipelines, and production monitoring. If they haven’t thought about these things, they haven’t really shipped AI.