AI Enablement Engineer

Shape the Future of Privacy

Usercentrics is a global leader in data privacy and privacy-led marketing solutions. We believe there is no need for a trade-off between growth and privacy compliance. Our vision is to unlock the potential of data privacy to empower a thriving digital ecosystem. We work with companies to create a healthy balance between data-driven business and privacy-led marketing for every size of enterprise.

Our customers build trust with their users through improved transparency and control to drastically improve marketing and monetization, while achieving full privacy compliance.

The Office of AI owns the strategy, governance, infrastructure, and enablement of AI across Usercentrics. We sit within the CTO office and work across all departments.

You are the senior technical owner of AI enablement at Usercentrics: the person who takes the hardest department AI builds, the deepest platform problems, and the technical bar the rest of the enablement track works to.

The role has two layers, and we mean both. At the department layer, you do what every AI Enablement Engineer does, prototyping and shipping AI applications for Finance, Legal, HR, Marketing, Customer Success, BI and beyond, but you take the hardest ones yourself and set the pattern the others follow. At the platform layer, you own the infrastructure that makes all of that possible: the LLM access gateway, RAG infrastructure, model operations, evaluation frameworks, and the engineering standards product teams build against.

What you would be walking into: a real internal AI platform already adopted across the company, the AI Hub, MCP servers, Claude connector delivery, a skill hub, usage analytics, and low-code automation. The next layer is largely open. A gateway, RAG infrastructure, model operations, and evaluation frameworks are the current candidates, and you would have a strong say in what gets built and in what order.

Real adoption already exists, which is a rarer starting point than a blank slate.

The role asks for two things  are a deeply technical engineer who can architect and build these systems hands-on, at the hardest end of the department builds and the platform layer both. And you build a technical community: you raise the bar across the Enablement Engineers and beyond through education, mentoring, and reusable patterns. If you only have one of these, this is probably not your role.

You will work alongside the AI Enablement Lead, who owns the non-technical adoption pillar (the champion network, AI literacy, and the cross-department use-case pipeline). Your focus is the engineering depth behind that pipeline, from the department app someone is using tomorrow to the platform layer that will still be standing in two years.

On the shape of it: this is a small, senior team today, four to five people, and growing on both the engineering and enablement sides. You would be the most senior engineer on the enablement track and the technical bar would be yours to set.

• Take on the hardest department AI application builds yourself, and set the technical pattern the AI Enablement Engineers work to
• Own the platform layer that supports every department build: LLM access gateway, RAG infrastructure, model operations, and evaluation frameworks are the current candidates, and you help decide what they are
• Own the internal AI marketplace: keep the cross-departmental AI apps secure, maintained, and governed as the number of them grows
• Own evaluation frameworks and the guardrails that let the team move fast without shipping unreliable AI: output quality, hallucination detection, regression testing
• Set the engineering standards and integration patterns for AI that product and department teams build against
• Raise the engineering bar across the org. Run technical education and enablement for engineers, document reusable patterns, and mentor builders beyond your own team
• Partner with the AI Enablement Lead, Security, and Platform Engineering to ship AI capabilities that are safe, governed, and adopted
• Translate company-wide AI goals into concrete engineering roadmaps and measurable outcomes

• 8+ years in software engineering, with significant time at staff level owning complex systems end to end
• Staff-level technical depth. You can own the most complex problems on the enablement track, from a department build to platform infrastructure, and lead by being the strongest engineer in the room
• Genuinely hands-on with AI. You have built and shipped production systems with LLMs, AI agents, RAG, or applied ML yourself, at both application and infrastructure layers
• A strong engineering foundation in distributed systems, backend platform design, and cloud architecture (Azure or GCP), with the judgement to set standards others follow
• A track record of mentoring senior engineers and unblocking ambiguous…