The AI revolution is accelerating, but Europe is struggling to keep up and risks falling behind in AI innovation. According to the latest data, 75% of European employers struggled to fill AI-related roles and this gap is growing every year.
The reason for that is that traditional software development and data science training do not fully prepare professionals for AI engineering roles. To close the skills gap, aspiring AI engineers need a new approach to learning – one that is hands-on, adaptable, and deeply connected to the AI community.
AI engineers occupy a critical space between software developers and AI researchers, applying AI advancements to build functional products. In fact, IBM reports that only 24% of software developers rate themselves as "experts" in generative AI.
"AI engineers are the link between AI researchers and real-world products," says Giedrius Žebrauskas, Chief Academic Officer at Turing College. "The shortage of people in these roles puts Europe in a difficult position where AI engineering tasks are given to software developers, ML engineers, and data scientists who lack the necessary skills. And because AI moves so fast, trying to learn alone can feel overwhelming."
According to Žebrauskas, the key to solving this issue is practical, up-to-date training that not only gives practice with state-of-the art technologies, but also teaches AI engineers how to stay ahead of market developments. Community-based learning is essential – working alongside others in the field allows engineers to pool knowledge and keep pace with technological advancements.
What makes a good AI engineer?
AI engineers stand at the intersection of software development and AI research. Their role is not to create new AI models from scratch but to integrate and optimise existing ones to develop AI-powered products. That means they need to stay on top of the latest AI breakthroughs and have the technical skills to translate research into usable solutions.
Demand for AI engineers is surging, particularly in two key areas. Established companies are trying to integrate AI features into existing products but lack in-house expertise. Meanwhile, AI-first startups need engineers who can quickly apply AI research to create competitive products.
"Companies are realising that AI development requires specialists," Žebrauskas explains. "A generalist software developer may take on an AI task, but without the right expertise, they won't execute it well – sometimes without realising their own limitations. It's like assuming a carpenter can build a quality brick house. They may put something together, but it won't be structurally sound. Since experienced AI engineers are still rare, we need to train more of them to fill the gap."
Where are the training programmes?
Despite the growing need, AI engineering training options in Europe are still limited. Platforms like Coursera offer some courses, but most lack the structured, community-driven approach that AI engineers need. This is where Turing College is stepping in.
Turing College's AI Engineering programme combines self-paced online learning with real-world projects and one-on-one mentorship. Learners also get access to an exclusive startup accelerator powered by the Baltic venture capital fund Firstpick, which provides the tools and support needed to turn AI-driven ideas into successful businesses.
