An AI engineer builds software that uses models to solve a user problem. The work includes defining the task, preparing data, connecting a model to an application, checking quality, and operating the result. Calling a model API is one step in that larger process.
Imagine a support team that needs answers from policy documents. The engineer decides how documents enter the system, how users are authorized, which evidence is retrieved, how answers are checked, and what happens when the model cannot answer. A working system needs ordinary software engineering as well as AI concepts.
You do not need to train a foundation model to begin. Start by learning programming and application boundaries, then learn model behavior, retrieval, evaluation, and deployment. This roadmap includes those foundations rather than assuming you already know Python.
Exercise: describe one user problem, the input, the useful output, and one way the system could fail. Identify which parts need a model and which can use ordinary code.
Check: your explanation describes a working product and a measurable outcome, not just a chatbot interface.