What AI Roles Should You Be Hiring for Right Now?

If you’re building with AI and not thinking about these roles, you’re already behind.

Let’s be honest. Most companies say they’re “doing AI,” but behind the scenes? It’s a Slack thread, a couple of notebooks, and someone hoping a chatbot can write a roadmap.

Real AI requires real people. It’s not one hire. It’s not a generic “AI developer.” It’s a team made up of specific roles that know how to take something experimental and turn it into something that ships, scales, and doesn’t get flagged by legal.

If you’re hiring (or trying to figure out who you should be hiring), here’s who actually matters right now.

Machine Learning Engineer

This is your builder. The person who trains, tunes, and ships models that don’t collapse under pressure.

They work with real data, make real decisions about architecture, and understand how to get something from dev to production without it falling apart in the process.

McKinsey lists this as one of the top three hardest roles to fill in AI right now. That’s not hype, it’s a hiring market that’s gotten brutally competitive. If you find a good one, don’t hesitate.

Data Scientist

Still essential, still short in supply. A good data scientist doesn’t just build models; they know when not to. They can look at raw data and tell you what’s worth pursuing and what’s noise.

Half of the companies using AI say they don’t have enough data scientists. That’s not a skills gap, that’s a business bottleneck.

MLOps Engineer

Most AI initiatives don’t fail due to faulty models or systems. They fail because no one could get it into production and keep it running. That’s where MLOps comes in.

This person handles deployment, automation, monitoring, and performance. They’re the reason your model still works a month from now. Not glamorous, but critical, especially at scale.

Prompt Engineer

This one’s new, but real. If you’re building anything with large language models, someone needs to understand how to craft inputs, tune outputs, and work with retrieval systems.

They’re not just writing prompts. They’re shaping how your model performs. According to McKinsey, 7% of AI-using firms already have prompt engineers on staff, and that number is growing fast.

AI Product Manager

Building smart tools is great. Building smart tools that people actually use? That’s what this role is about.

A strong AI PM understands technical limitations, business priorities, and user expectations. They don’t just ship features they manage risk, coordinate across teams, and make sure you’re solving real problems, not just showing off.

Responsible AI Lead / Compliance Officer

It’s not just about being “ethical.” It’s about staying out of trouble with regulators, customers, and your own legal team.

These are the people who think about bias, explainability, and what happens when your model makes a mistake. McKinsey reports that 13% of firms have already hired AI compliance roles, and that number will climb as AI laws continue to tighten.

So What Now?

The roles have changed. The stakes are higher. The skills are more specialized. And the companies getting this right? They’re not just hiring faster, they’re hiring smarter.

The good news: the talent is out there. You just have to know where to look and what you’re actually looking for.

Need help with that? That’s what we do. Click here and let’s talk.