Hiring AI Talent: Beyond LLMs to Real-World ImplementationHiring AI Talent: Beyond LLMs to Real-World Implementation
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CJ Wooden
• Jun 3
Hot take:
The next hiring competition in AI won't be won by companies chasing "LLM experience."
It'll be won by teams that know how to find people who can make AI work after the demo is over.
A year or two ago, most AI hiring conversations sounded the same: Have they worked with LLMs? Do they know OpenAI APIs? Can they prompt? Have they fine-tuned models?
Those things still matter. But they're no longer enough.
The companies building real AI products need people who understand systems. People who can deal with messy data, unstable infrastructure, weird model behavior, latency issues, cost pressure, and users who never behave the way the demo expected.
That's a very different profile from "has AI keywords on their resume."
This shows up constantly in robotics, applied ML, AI infrastructure, data platforms, and agentic workflows.
The hard part usually isn't getting a model to do something impressive once. The hard part is making it useful, reliable, and affordable in the real world.
That should change how founders hire.
Instead of asking "does this person have AI experience?" ask:
Can they reason through a system they didn't build?
Can they make progress while requirements are still shifting?
Can they troubleshoot across data, infrastructure, product, and model behavior?
Can they take a promising demo and turn it into something dependable?
For consultants and advisors, this comes up a little differently. A client says they need "AI talent," but what they actually need might be an ML engineer, a data engineer, a backend infra person, a robotics software engineer, or someone who can connect all of those pieces. The real value is helping them define the actual problem before they waste weeks building the wrong pipeline.
Vague searches create vague candidate pools.
The teams that win this next phase won't just hire people who can talk about AI. They'll hire people who can ship it.
That's a different talent market — and most hiring strategies haven't caught up yet.
If you're hiring for AI, ML, robotics, infrastructure, or data roles and the market feels noisy, I help teams get clear on what they actually need before they spend weeks chasing the wrong candidates.
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