Why companies are increasingly hiring engineers who can apply AI, rather than AI specialists alone
Artificial intelligence continues to dominate technology conversations.
Boards are discussing it, investors expect it, and almost every technology leader is considering how AI can improve products, operations, and decision-making.
Unsurprisingly, this has influenced hiring.
Over the past two years, many companies began talking about building AI teams and hiring specialist machine learning talent. In some cases, that was the right approach.
But in many of the conversations I’ve had recently with CTOs and engineering leaders in Germany, the focus has become much more practical.
The question is no longer simply, “How do we build an AI capability?”
Instead, it is increasingly, “How do we apply AI within the systems we already have?”
From Experimentation to Implementation
Early AI initiatives were often exploratory.
Companies wanted to understand what the technology could do, build proof of concepts, and experiment with new use cases.
That phase is still important, but many organisations are now moving beyond experimentation and into implementation.
They want to integrate AI into products, automate internal processes, improve customer experiences, and increase developer productivity.
That shift changes the type of people they need to hire.
Why Strong Engineering Fundamentals Matter
In practice, implementing AI is rarely just an AI problem.
It is a software engineering problem.
AI features still need to be integrated into existing architectures, connected to APIs and databases, deployed securely, monitored in production, and maintained over time.
For that reason, many companies are prioritising engineers with strong fundamentals in:
- software architecture
- distributed systems
- cloud infrastructure
- data engineering
- security
AI knowledge is increasingly valuable, but it often sits on top of these core engineering capabilities.
The Rise of Hybrid Profiles
One of the most interesting trends in the market is the growing demand for hybrid profiles.
Companies are looking for engineers who combine traditional software engineering experience with an understanding of how AI can be applied in practice.
They may not be world-class researchers, but they know how to turn new technologies into working systems that deliver business value.
In many cases, this is exactly what organisations need.
What This Means for Hiring
The strongest AI hires are often not pure specialists.
They are experienced engineers who understand architecture, production systems, and delivery, and who are comfortable incorporating AI into real-world products.
This is one reason why many of the AI-related roles I’m seeing still look fundamentally like senior software engineering or technical leadership positions.
The difference is that AI is becoming an increasingly important part of the stack.
AI as an Engineering Capability
Over time, AI is likely to become less of a standalone discipline and more of a standard capability embedded across engineering teams.
For employers, this means the challenge is not simply to find “AI talent.”
It is to identify engineers who can combine strong technical foundations with the ability to apply AI effectively.
And from a hiring perspective, that is a very different search.