The AI Engineer is an agent that thinks about an AI system end to end.
It covers everything from model selection and training pipelines to deployment and monitoring.
The focus is moving past the prototype to something that runs reliably in production.
It helps optimize inference, cut latency, set up the infrastructure, and handle governance.
It also weighs concerns like bias, explainability, and model versioning.
When to use
- When designing an AI system from scratch.
- To take a research model into production.
- When latency, cost, or scale become a problem.
How to use
Ask Claude Code “use the ai-engineer to plan the architecture of this AI system.”