Head of AI Engineering (M/F/D)

Your missions

We’re looking for a Head of AI Engineering to lead JobTeaser’s AI chapter — and make it real.

This is a new role. We’re creating it now because we believe AI is not a feature add-on or a productivity tip. It’s a structural shift in how a tech company works and builds products. For the first time, we’re putting real budget, a dedicated team, and full exec commitment behind it. The bar we’ve set for ourselves is simple: if in six months our teams still work the way they do today, we’ve failed.

You will manage 3 people: 2 ML Engineers building AI into our product, and 1 AI & Automation Evangelist driving how our 200+ colleagues use AI in their daily work. Two distinct mandates, one shared goal. You’ll report directly to the CTPO and be the person who turns our AI ambition into something people can actually feel.

What you’ll do

Own the AI vision

• Define and maintain JobTeaser’s AI blueprint across both dimensions: AI embedded in our product (matching, guidance, student experience) and AI transforming how our internal teams work.

• Partner with every department to translate ambition into a sequenced, realistic plan — not a slide deck.

• Set the success standard, track it honestly, and report progress to the CTPO with a clear picture of what’s working and what isn’t.

• Keep the strategy connected to what our users and colleagues actually need, not just what the technology makes possible.

Lead and grow the team

• Be the direct manager of 2 ML Engineers and 1 AI & Automation Evangelist.

• Run regular 1:1s, give honest feedback, and actively support each person’s development.

• Be the technical anchor your ML engineers can look up to — someone whose engineering judgment they respect, not just someone who manages their tickets.

• Create the conditions for each team member to go further than they would alone.

Drive internal AI adoption

• Work hand in hand with the AI & Automation Evangelist to design and roll out a real internal transformation — not a series of isolated use cases, but a genuine shift in how people work.

• Engage managers first, teams second. You know how change actually happens inside organizations.

• Identify with department heads where AI creates the most leverage, then make it happen.

• Track adoption honestly: active users, workflows changed, time saved. No vanity metrics.

Ship product AI and stay hands-on

• Oversee the two ML Engineers on product-facing features: recommendations, matching, student-facing AI tools.

• Stay close enough technically to review architecture decisions, challenge model choices, and contribute directly when the team hits a hard problem.

• You’re not expected to write most of the code — but you can. When a key prototype needs a second pair of hands or a critical architecture call needs to be made, you’re there.

• Keep your technical judgment sharp: you know what current models can and can’t do, you have opinions on infrastructure trade-offs, and you bring that into every important decision.