Modern Hiring Criteria for AI: What 50+ Senior AI Jobs Really Demand

But the way companies write these job descriptions hasn’t caught up. They list stacks of keywords and rigid requirements, but over and over, what they actually need is broad engineering experience and proven leadership, not just AI-specific tricks—the skills employers actually value.

So what’s at stake? It changes everything about how we hire and how we position ourselves. Getting stuck in the checklist mentality means breaking rigid job requirements and missing the people who can actually push things forward, especially in a space that evolves this quickly. I’ll dig into how to use this gap to your advantage, whether you’re hiring or applying.

What’s Written vs. What’s Wanted

Most job posts, even as of this quarter, are still Frankensteined together—copying requirements from other listings, old partner orgs, or whatever looks standard on LinkedIn. I see legacy asks thrown in that clearly don’t fit. Some listings want you to have spent ten years working on tools that have barely existed five. The reality? They want seasoned engineers who can figure out AI, not AI specialists who happen to code.

Here’s where the numbers get interesting. Nearly every listing expects hefty general engineering experience—the median ask is for 10 years, minimum—but only 19% specify any number of years working directly on AI systems. That gap tells me most companies haven’t actually mapped their needs to the skills that work in production. They’re embracing versatile AI hiring strategies by looking for people who’ve built things, not just folks who passed a trendy bootcamp.

I still have to shake my head when I see a must-have line for “deep experience in RAG” or “multi-agent workflows.” How do they know they need RAG if they haven’t worked through the basics? Sometimes I’m convinced these requirements are borrowed straight from a competitor’s post—no one in the room has actually shipped what’s on the list.

Here’s the trap: requiring the right phrase or certificate doesn’t mean you’re getting someone who can do the work. I’ve seen countless roles with dozens of specializations listed out, but nobody agrees on what an AI engineer actually needs to know beyond LLMs, Agents, and RAG. In a field where what matters changes every few months, track record and AI talent adaptability beat any credential you can type into a box.

Weird detail—one job asked for “multi-turn LLM dialogue experience” right after a bullet about TensorFlow 1.13 (which, if you remember, is pre-pandemic tech). I spent a few minutes trying to figure out how those would even overlap in production, then realized it probably made as much sense to them as it did to me. Sometimes I wonder if anyone reads these after they’re posted, or if it all just snowballs from template to template.

Team Builders Over Tinkerers: The Real Differentiators

If you strip out the recycled boilerplate in these job postings, two things remain almost universal. Team Building showed up in 56% and Technical Leadership in 51% of them, clearly emphasizing the importance of hiring for leadership skills. That’s way higher.