An AI Engineer job posting almost never asks you to train a model. It asks for Python. Backend services. APIs. Deployment.
Say you are two years into your career. You want the AI Engineer title, because that is where the hiring is right now. You open a listing. Under requirements you find things you already know: REST APIs, Postgres, a message queue. Then one line stops you: experience with LLM-based retrieval systems. You have never trained a model, so you assume the role is out of reach. You close the tab and go back to applying for titles that do not say AI.
Open ten AI Engineer job listings yourself. Count how many ask you to design a training pipeline. Count how many ask you to call a model through an API, store some vectors, and ship a working service. The ratio is not close. And unlike most claims you will read this week, that is one you can check in ten minutes on any job board.
That gap between the title and the requirements is not a trick played on the applicant. It is a plain description of what the job actually is. Model training is real machine learning work, PhD-deep, and it happens at a small number of companies with the budget and the data to do it. Everyone else shipping something called an AI feature is doing something else entirely.
