The skills that show up in every AI job posting right now

Save a handful of AI/ML postings and a pattern emerges fast: Python and some flavor of an LLM framework are close to universal. After that it fragments — vector databases, agent frameworks, and cloud platforms show up often but not consistently, varying by company and team.

That fragmentation is useful information on its own. It suggests the highest-leverage thing to learn is whatever's common across postings you're actually targeting, not whatever's trending broadly — the two lists aren't always the same.

It also varies a lot by company stage. Earlier-stage companies more often name a specific framework or vector database by name, because a small team has already committed to one and wants someone who's used it. Larger companies more often describe the work in terms of outcomes — "build and evaluate retrieval pipelines" — and leave the tooling unspecified, since internal platforms vary team to team.

That distinction matters for how you prepare. If the postings you're saving skew toward named tools, going deep on that specific stack pays off. If they skew toward outcome language, the more transferable skill is the underlying pattern — how retrieval, evaluation, or agent orchestration actually works — since you'll likely be picking up whatever internal tooling exists on day one anyway.

This is the whole idea behind RoleScan's Learning Topics chart: it's not a generic "skills to learn in 2026" list, it's built from the postings you personally saved, so the signal is specific to the roles you're actually going after.