Insights & resources

Look for evidence of shipping models to production, not just training them. Ask about data quality problems, monitoring and how they measured success. Strong candidates explain trade-offs clearly and work well with software and data teams.

Generative AI teams usually combine software engineers who build applications, ML engineers who adapt and evaluate models, data engineers who prepare content, and product leaders who define the use case. Start by clarifying the problem, then hire the roles that solve it.

Reliable AI needs versioned data and models, automated testing and deployment, and monitoring for drift. Pair ML engineers with platform or DevOps engineers early so prototypes do not stall.

Choose contract engineers for defined projects and peaks, contract-to-hire when you want to test fit, and full-time hires for core products you will own for years. Many teams blend all three.

Review fundamentals for your role, practice explaining your thinking out loud, prepare two or three project stories with measurable results, and ask thoughtful questions about the team and codebase.

Use work samples, structured interviews and practical exercises that mirror the job. This widens your talent pool, reduces bias and predicts on-the-job performance better than resume keywords.