What is an AI Engineer?

An AI engineer is a software developer who builds products on top of foundation models — large pretrained models exposed through APIs from providers like OpenAI, Anthropic, and Google, or run locally as open-weight models. The role emerged when capable models became available as a service: suddenly you could add summarization, chat, extraction, or code generation to a product without owning a GPU cluster or a research team. The craft is in integration, not model training.

This matters because it is one of the fastest-growing engineering roles, and it is genuinely accessible to working developers. If you can build a web backend, you already have most of the prerequisites. What you add on top is model-specific judgment: writing effective prompts, designing retrieval pipelines, orchestrating agents and tool calls, evaluating outputs, and managing cost and latency. Companies are hiring for exactly this blend, and data scientists are transitioning into it too — often finding the software engineering half harder than the AI half.

Day to day, you will call a chat completions or messages endpoint from your backend, engineer the system prompt, wire the model to your database and external tools, and write evals that catch regressions when you swap models or edit prompts. You will treat the model as a probabilistic dependency: powerful, but needing guardrails, retries, and monitoring. The rest of this module breaks the role down further, starting with concrete responsibilities.