AI Careers
Nobody is hired for what they have watched. They are hired for evidence, and evidence means something running that you can explain.
The gap between studying and being hireable
The common path is courses, then more courses, then a certificate wall and no offers. The courses were not wasted — they were just not evidence. An employer cannot verify what you understood from a video, and a completion badge tells them only that you reached the end.
What they can verify is code that runs, a decision you can defend, and a problem you solved end to end. That is why one finished project moves an application further than four certificates, and why the order matters: build first, then learn what the build exposed.
What is actually checked
Whether your repositories open, and whether the README explains how to run the thing. Whether your explanation of a project matches its code. Whether you can name a trade-off you made and say what you gave up. And increasingly, whether you can work with AI tools honestly — using them and understanding the output, rather than either pretending you do not or shipping code you cannot read.
Almost none of that is about which framework you know. Frameworks are learned in a week; judgement is what the interview is for.
Where this usually goes wrong
Collecting certificates as a substitute for building
A certificate is a receipt for attendance. It is worth something next to a project and very little instead of one.
A CV listing technologies rather than outcomes
"Python, TensorFlow, LangChain" says you have seen them. "Cut document processing from 4 minutes to 20 seconds" says what you did.
Waiting to feel ready
The field moves faster than anyone can finish preparing for it. Applying while building beats building until you feel finished, because you never will.
Questions people actually ask
Is an internship certificate worth anything?
On its own, very little — including ours. What has value is the work behind it and the fact that a claim can be verified rather than taken on trust. A certificate next to a repository someone can open is evidence; a certificate alone is a PDF.
Do I need a degree in machine learning?
For research roles, usually. For the engineering roles that make up most of the market, demonstrable ability matters more than the route you took to it. Hiring managers filtering hundreds of applications look for evidence first.
What should I learn first?
Enough Python to build something, then one narrow area deeply — retrieval, or agents, or evaluation — rather than a shallow pass over everything. Depth in one area gives you something to talk about; breadth without it gives you nothing to say.
Build it, free
Project briefs with the assessment criteria published before you start, and written code review on what you submit. No payment to apply or to complete.