AI careers

The job titles in AI are still settling. The same posting might describe someone who fine-tunes models, someone who wires APIs into a product, someone who cleans and labels data, or someone who owns the decision of whether to use a model at all. That ambiguity is hard to navigate from the outside, and it is why generic advice about "getting into AI" tends to be useless.

These posts try to be specific instead. What a hiring manager reads first in a portfolio, and what they skip. Which skills transfer from analytics, engineering, design, or ops — and which genuinely have to be learned from scratch. How to pick a first project that demonstrates judgment rather than tool familiarity. What interview processes look like at companies that have shipped AI features versus those still deciding to.

There is also a fair amount on the parts people underestimate: writing clearly about what you built, scoping a project small enough to finish, and being able to explain why you chose an approach and what you would do differently. Those are the things that separate two candidates with identical course lists.

Read these next to the strategy posts if you are deciding what to build; the two questions are the same question from different sides.