The AI Engineer is the engineer who builds products and features on top of existing AI models, especially large language models (LLMs), and puts them into production reliably. They generally do not train models: they assemble them, connect them to company data and measure their quality.
Their work covers RAG (connecting a model to a document base), prompt engineering, designing agents and tool calls, automated evaluation of answers, observability, and managing cost and latency. They mostly use Python or TypeScript, model provider APIs (Anthropic, OpenAI, Mistral…) or open weights models, and vector databases.
The profile often comes from back-end or full-stack development rather than research.
Why it matters when hiring
The AI Engineer differs from the Machine Learning Engineer, who trains and optimizes models, and from the Data Scientist, who leans more toward analysis and statistical modeling. The strong signal: an AI feature in production with real users, and an evaluation method they can explain. A list of frameworks or demos without metrics is not enough. The role is hired as soon as a product puts generative AI at the core of its usage.