**About TechWolf and the AI team**
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TechWolf is on a mission to make work better and fairer for millions of people by powering the shift from jobs to skills as a framework to model talent. Our enterprise software uses AI to provide the skill data needed to power that transformation, for some of the largest employers in the world.

AI is not a feature in our product; it is the product. Our AI team owns everything from research through end-to-end production deployment: the inference engine that turns HR and work data into skill and task profiles, and the agents that put that intelligence to work inside our customers' tools. We prioritize fair, transparent and impactful AI.

**The role**
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We are looking for an AI Engineering Manager to lead two AI engineering teams. **Agents** builds the agentic layer of our product: agents and integrations that let customers work with their skill data in the tools they already use. **Inference** builds the AI pipelines and models that turn raw HR, work and labor market data into high-quality skill and task profiles at enterprise scale.

Each team has a team lead and a group of engineers, and both are growing, with AI Engineer roles open in each. You report to our Director of AI, Jens-Joris, manage the two team leads directly, and work alongside the Engineering Managers of our Platform and Integrations domains.

This is primarily a people and delivery role: you own team performance, hiring and engineering processes, not the bulk of the code. You do need the technical depth to challenge architecture decisions, judge trade-offs in AI systems, and earn the trust of strong AI engineers.

**What you'll do**
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* **Raise the talent bar:** Hire AI Engineers for both teams, give clear feedback, and run fair performance conversations. Act early on both strong and underperforming work.

* **Grow the team leads:** Coach the leads of the Agents and Inference teams in their own leadership, so each team runs its day-to-day with growing autonomy.

* **Own delivery:** Make sure both teams plan realistically, flag risks early and ship predictably. Balance roadmap work, reliability, and technical debt across the two teams.

* **Set engineering standards for AI:** Refine engineering processes for AI work specifically: evaluation before release, observability in production, cost control on LLM and inference workloads, and short loops from experiment to production.

* **Connect the teams:** Be the bridge between your teams, Product, our Research team, and the other engineering domains, so priorities and dependencies stay aligned.

* **Challenge the architecture:** Review system design with your teams and make sure what we build today scales to the next order of magnitude in customers and data.