AI is changing tech hiring, but the job titles are still catching up.
AI engineer. AI agent engineer. Machine learning engineer. Data scientist. Forward deployed engineer.
Some of these roles are new. Some are old roles with new tools. And some get used interchangeably even when they should not.
In this episode of Stacked: Tech Hiring Insights, Matt Mulcahy breaks down what these AI and engineering roles actually mean, how companies are hiring for them, and where the market is headed.
AI Engineer vs. AI Agent Engineer
AI engineers are often focused on developing, training, or customizing models.
AI agent engineers are focused on building the systems, tools, and workflows that use those models to do something useful.
That distinction matters. One role is closer to the model. The other is closer to the execution.
Machine Learning Engineers Still Matter
Machine learning engineers did not disappear.
Many have evolved into AI engineering or AI agent engineering roles. Others are still focused on building models, analyzing data, and helping systems make predictions or decisions.
The title matters less than the work. Companies need to understand what they are building before they decide which skill set to hire for.
Data Is Still the Foundation
AI does not fix bad data.
If your data is messy, your output will be messy too. Clean data, strong architecture, and reliable internal systems are still critical.
Before companies build AI tools, agents, or customer-facing features, they need to make sure the foundation can support it.
Forward Deployed Engineers Are Gaining Ground
Forward deployed engineers are becoming more important as technology roles get closer to the customer.
These engineers are not just writing code. They are gathering requirements, scoping work, writing documentation, talking to clients, and building the solution.
As AI products become more practical and business-specific, that mix of technical and client-facing ability is getting more valuable.
What Companies Are Hiring For
Hirewell is seeing AI hiring show up in a few key areas:
Internal automation
AI-enabled engineering teams
AI-driven product development
Those are very different needs. The right hire depends on whether a company is trying to improve internal processes, make engineering teams more efficient, or build AI-powered products for customers.
The Bottom Line
AI is becoming part of every tech hiring conversation.
Companies are asking candidates how they use AI tools, what they think of them, and where those tools still fall short.
The market is not waiting for perfect job titles. The companies hiring well are the ones getting clear on the work first, then finding the talent that fits.
Watch the full episode here: https://talentinsights.hirewell.com/
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