Hiring guide
How to hire an AI developer for a production product
Select an AI developer who can connect model behavior to a reliable product workflow and operating controls.
Updated September 2026
Quick answer
Ask how the developer handles failures, evaluation, data access, latency, cost, and model changes. A production AI developer should explain the full workflow, not only prompts or model APIs.
Evidence and decision points
Product integration
The developer should connect models to authentication, data, interfaces, and user decisions.
Failure handling
Ask what happens when a provider times out, a tool fails, or an answer lacks evidence.
Operating controls
Production work needs limits, monitoring, evaluation, and clear human approval points.
Start with the user outcome
A candidate should define the user task before selecting a model. The workflow, data, latency, quality, and cost requirements should guide the model choice.
Ask the candidate to describe one case where a simpler workflow was better than adding an agent.
Test production judgment
Use a paid task that includes a normal result and one failure state. Require a short explanation of data handling and evaluation.
The candidate should distinguish deterministic application behavior from variable model output.
- Define representative inputs and expected outcomes.
- Handle timeout, rate-limit, and invalid-output states.
- Record latency, model use, failures, and costly steps.
- Keep external actions behind explicit permissions.
Check security and ownership
Ask how the system prevents private data from crossing access boundaries. Tool access should use narrow inputs and explicit permissions.
Confirm code ownership, provider accounts, deployment access, documentation, and handover terms before the engagement starts.
Prototype skill versus production skill
| Area | Prototype answer | Production answer |
|---|---|---|
| Quality | The output looks good | Representative evaluation cases |
| Failure | Retry the prompt | Defined user and system recovery |
| Data | Send context to the model | Access, retention, and provider boundaries |
| Cost | Choose a cheap model | Measure workflow cost and set limits |
Sources and further reading
- NIST AI Risk Management Framework
NIST provides a useful structure for AI risk, measurement, and governance questions.
- AI developer hiring discussion
The discussion emphasizes product integration, production experience, and failure handling.
Common questions
Do I need a machine-learning researcher?
Not always. Many products need an applied AI engineer who can integrate models into a reliable product workflow.
How should I test an AI developer?
Use a bounded paid task with representative inputs, one failure state, and written acceptance checks.
Should the developer support several providers?
Only when product needs justify it. Extra providers add routing, testing, failure, and operating work.
Related resources
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Email Amar