AI Lead Engineer - Enablement

Job Description

Working with the CIO, our Trading & Sales Entities and our Digital Transformation Office this position will allow you to drive the building and adoption of our AI practices.

The AI Enablement practice you will set up and embody has the mission to enable our delivery and business teams across the business units to elevate their delivery pace and quality. You do this through solid, reliable and scalable engineering practices and through AI agents built on (e.g.) a solution like Claude or Copilot.

This is an individual contributor role, and you'll be the first dedicated person in this function at MET Group — you're building the practice, rather than joining an established one. It's a hands-on builder and coach role: someone who can sit with a business or engineering team, figure out what could accelerate them, and turn that into e.g. a reusable agent, a tightened pipeline, or a more frictionless way of working. Through this you will drive the maturity growth of the use of end-user-AI productivity solutions. Depending on adoption and maturity progress, there is the natural next objective to developing enterprise grade, governed, autonomous agentic workflows on our enterprise data/ analytics platform.

Essential responsibilities

• Strategy/ Direction: Over time co-evolve the MET Group AI strategy with CIO and key stakeholder. To do this you keep yourself up to date with market developments, including cost developments and open-weight/make/buy considerations.
• Hands-on discovery/prototyping: Work directly with delivery squads across MET entities to diagnose where engineering practice or manual work is costing them time, and design agentic / machine learning or automated solutions for it.
• Engineering Enablement / Agentic Development. Increase quality, security and value delivery, using agentic loops in CICD pipelines. You put in place observability practices and evals
• MLOps, you are familiar with and drive quality/standardization on the following, use of a central feature-store, training data sets, eval data sets, model drift eval. Approach, tooling.
• Change Management/ Adoption: You effectively bring our organization and employees along in their AI adoption journey, you listen, coach, and design and measure adoption of AI practices.

Qualifications

• 6+ years of hands-on industry experience in software engineering and AI/data science developing ML solutions and AI agents for end-user-AI tooling (e.g. Claude) and enterprise agentic AI (prompt/agent design, tool use, evaluation), and fundamental understanding of and experience with building data science solutions in general.
• Degree in Data Science, Artificial Intelligence, Software Engineering 
• The ability to connect in various contexts (Sales, Trading, Technology, Senior Management) and ask the right questions to get to key insights that allow for strategic analysis and business case development
• A coaching instinct: you guide, teams, management to build their own skills to become more and more autonomous

Nice to have

• Experience with: Databricks, Azure Cloud, Azure Foundry, agent-orchestration frameworks and LLMOps, MLOps, Azure-agentic development tooling, LLM-Gateways (openRouter)
• Exposure working in the B2C/B2B energy industry

Additional Information