Q: What does an AI Engineer actually do?

An AI Engineer builds software applications that have an AI model as their brain, and gets them into production.

The brain is an LLM, a Large Language Model — the technology behind ChatGPT, Claude, and Gemini. The model itself already exists and runs on the servers of OpenAI, Anthropic, or Google. Your job is everything around it: connecting to the model through APIs, feeding it the right context (RAG, embeddings, vector databases), giving it tools so it can take actions, and orchestrating agents.

But here's the part most people underestimate, and it's the part that actually defines the role: deployment. A prototype running on your laptop is not a product. The real work is getting that system into production: deploying it to the cloud, securing it, monitoring it, controlling its costs, and keeping it reliable when real users hit it. I often tell engineers: 95% of the job is understanding how to deploy — and the job market agrees. An analysis of 1,000+ AI Engineer job descriptions found roughly 95% are production-focused, with RAG, agents, evals, and deployment dominating the requirements.

A typical week looks like normal software engineering with an AI core: writing Python, designing how data flows into the model, testing output quality with evals, debugging agent traces, reviewing cloud costs, shipping features. You're not doing research, and you're not training models. You're building products.

If you want the one-sentence version for a dinner party: ChatGPT is a brain in a jar; AI Engineers wire brains like that into real applications that companies actually use, and keep them running.