The AI Role That Doesn't Exist on Paper Yet
My job title is AI Solutions Developer. There was no listing. No hiring committee. The role didn't exist until I made it exist by doing work nobody else could do and proving it was worth formalizing.
I think that's going to be the path for most people in AI automation over the next few years. The roles that matter aren't cleanly defined. They sit at the intersection of domain expertise and technical execution, and most companies can't write a job description for that because they haven't seen it done.
The pattern I keep seeing: companies hire AI engineers who don't understand the business, or domain experts who can't build anything. Both underperform. The engineer builds impressive demos that don't solve the actual problem. The expert identifies the right problems but can't ship without a dev team and a six-month roadmap.
The person who does both is almost impossible to hire. They're rare, and they probably already have a job. Or they built their own.
I came from enterprise sales. Nearly a decade managing a portfolio of financial institution accounts. I know how to run a technical discovery, manage a proof of concept, and hold a room with both the security team and the finance team without losing either. That background isn't separate from my AI work. It's foundational. I build better automations because I understand what the end user actually needs, not just what's technically possible.
If you're someone with domain expertise and you're learning to build with AI: stop waiting for the right job posting. Build things that solve real problems in your domain. Document them. Show the before and after.
The resume line that moved the needle for me wasn't about technology. It was a business outcome. That's what gets a hiring manager's attention.
Nobody cares that you can call an API. They care that you used it to solve a problem worth solving. The role might not have a name yet. Build it anyway.
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