Hire AI Developers who ship production-ready AI features

Assess production experience

Ask candidates about AI features they have shipped to real users, how they measured quality and how they handled failures. Prototype experience alone does not prepare someone for production constraints such as cost, latency and security.

Use a realistic technical task

A small task, such as building retrieval over a set of documents with an evaluation script, reveals far more than algorithm puzzles. Look for clean code, sensible trade-offs and tests.

Clarify ownership and review

Decide who reviews AI-related code and approves changes to prompts and evaluation sets. Prompts are part of your codebase and deserve the same review discipline.

Plan for data access early

AI features often need access to sensitive data. Agree data handling rules, environments and permissions before development starts to avoid delays and risk.

Budget for evaluation and monitoring

Allocate time for building evaluation sets and monitoring, not just features. This investment is what keeps AI features reliable after launch.

A sample job summary for an AI developer

A clear summary might read: we are hiring an AI developer to build AI features into our SaaS platform, including retrieval over customer data and an assistant that answers questions in plain English. You will integrate model APIs, design evaluation sets, monitor quality and cost, and work within our TypeScript codebase and review process. This role suits a pragmatic engineer with production experience who cares about reliability and security as much as capability.

Red flags when hiring AI developers

Be cautious of candidates whose experience is limited to demos and tutorials, who cannot explain how they measured answer quality or who dismiss security concerns such as prompt injection. Production AI requires discipline around evaluation, monitoring and data separation. Another warning sign is attachment to one model or framework regardless of fit. Strong developers compare options against your requirements and keep systems flexible.

What a good first week looks like

Your AI developer sets up a development environment, reviews the codebase and existing AI experiments and meets product and engineering leads. They read your AI roadmap and propose an approach for the first feature, including how it will be evaluated. A small first contribution, such as improving an existing prompt with tests or adding logging, helps them learn your processes. By the end of the week, there should be an agreed technical plan.

Building AI capability for the long term

Hiring AI developers is most effective when the rest of the organisation is ready to support them: clear product goals, access to relevant data, and engineers and product managers willing to review AI behaviour as carefully as code. Treat prompts, evaluation sets and monitoring as long-lived assets. Over time, these practices let your team ship AI features confidently and adapt as models improve.

Tools and access to prepare for your AI developers

Provide access to your code repositories, issue tracker, documentation and a development environment with test data. Set up API keys for the model providers you use under your organisation's account, with spending limits and monitoring enabled. Share your security policies, data classification rules and any compliance requirements, along with the product roadmap and existing AI experiments. Introduce the developer to the engineers and product managers they will work with most closely.

If production data is needed for evaluation, agree how it will be anonymised or accessed safely before work begins.

Measuring your AI developer's contribution

Combine standard engineering measures, such as features delivered and code quality, with AI-specific ones: evaluation scores on your test sets, production error rates, latency, cost per request and user feedback on AI features. Improvements in evaluation scores before release and stable quality after release show disciplined work. Review these metrics in your normal engineering cadence so AI features are held to the same standards as the rest of your product.

Interview questions to ask a AI developer

1. How would you design retrieval over thousands of internal documents?
2. How do you evaluate whether a change to a prompt improved results?
3. How do you defend against prompt injection?
4. Tell me about an AI feature you shipped and what you would do differently.
5. How do you reduce model costs without hurting quality?
6. How do you keep customer data separate in a multi-tenant AI feature?
7. When would you use a smaller model?
8. How do you document AI system behaviour for your team?

A 30, 60 and 90 day plan for your new AI developer