• What you doBuild ML and AI models: recommendation, prediction and generative use cases
• Set up ML pipelines and take models to production, including training, deployment and retraining
• Put monitoring and observability in place for AI workloads, and watch performance, latency and reliability
• Integrate LLMs and AI APIs into working workflows, copilots and agents
• Translate business use cases into scalable solutions and advise on architecture choices

You work with data engineering, software engineering, product and external AI partners. A large part of your time is spent in direct contact with the business.

• What you bring4 to 7 years in ML engineering, AI engineering or platform engineering
• A foundation in machine learning, not only in LLMs. You understand what happens under the hood
• Strong Python and experience with production environments for AI
• Experience with LLMs, AI APIs, vector databases, orchestration frameworks and CI/CD on a cloud platform
• An understanding of data engineering and API architecture
• You can explain technical choices clearly to people without a technical background
• You do not need to tick every box. Come close and I would like to hear from you.You will enjoy this ifYou would rather build than advise, you like being somewhere the AI platform still has to take shape, and business impact matters more to you than research for its own sake. There is a lot of autonomy and very little that is already fixed.Nice to have