Why Job Descriptions Are Misleading
Many AI job postings list a long, generic set of buzzwords because the hiring team is still figuring out exactly what the role needs. The result is that the posting is a weak signal — the actual skill bar is usually narrower and more specific than the listing suggests.
The Skills That Show Up Again and Again
Across a wide range of AI-enabled technology roles, a small set of skills recur consistently: working with LLM APIs, retrieval-augmented generation, evaluation methodology, and the judgment to know when an AI feature is production-ready versus still a prototype.
Skills That Matter Less Than You'd Think
Deep machine learning theory — training models from scratch, understanding transformer internals at a research level — is rarely required for AI application, GenAI or MLOps roles. Most production AI work is closer to systems engineering than research.
How to Build Evidence, Not Just Claims
The skills above are only useful to an employer if you can demonstrate them. A completed project with a clear write-up of decisions and tradeoffs is far stronger evidence than a list of skills on a resume.