Hire AI/ML Engineers

We screen specifically for production experience, because the failure mode in this role is an engineer who can reach a good validation score but has never had to keep a model healthy for a year.

A typical week

• Framing a business problem as something a model can actually learn, or pushing back when it cannot
• Building and versioning training pipelines so results are reproducible months later
• Packaging models behind APIs with latency, cost and failure behaviour understood
• Watching drift and accuracy in production and retraining before users notice decay
• Explaining model behaviour and limits to stakeholders without hiding behind jargon

What we screen for

How we assess this role before anyone reaches your shortlist.

• Can they take a vague business ask and turn it into a measurable modelling problem
• Do they evaluate honestly — holdout discipline, leakage awareness, sensible baselines
• Have they operated a model in production, including a degradation incident
• Can they write maintainable Python that another engineer can pick up
• Do they know when a simple model or a rules engine is the better answer

Related specialisms

Frequently asked questions