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.AI (Enablement) Lead Engineer (m/w/x)
Building enterprise agentic AI and end-user AI tooling for an integrated energy company. 6+ years software engineering, ML solutions, and AI agents development required. Focus on accelerators and team enablement.
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Anforderungen
- 6+ years software engineering/AI/data science experience
- ML solutions and AI agents development
- End-user-AI tooling (e.g. Claude) experience
- Enterprise agentic AI (prompt/agent design, tool use, evaluation) experience
- General data science solutions building experience
- Degree in Data Science, AI, or Software Engineering
- Connecting in various contexts (Sales, Trading, Technology, Senior Management)
- Asking right questions for strategic analysis
- Business case development
- Coaching teams and management
- Guiding teams to build autonomy
- Databricks experience
- Azure Cloud experience
- Azure Foundry experience
- Agent-orchestration frameworks experience
- LLMOps experience
- MLOps experience
- Azure-agentic development tooling experience
- LLM-Gateways (openRouter) experience
- B2C/B2B energy industry exposure
Aufgaben
- Drive the building and adoption of AI practices
- Set up and embody the AI Enablement practice
- Enable delivery and business teams to elevate pace and quality
- Build the AI practice function
- Sit with teams to figure out accelerators
- Turn accelerators into reusable agents, tightened pipelines, or frictionless ways of working
- Drive maturity growth of end-user AI productivity solutions
- Develop enterprise-grade, governed, autonomous agentic workflows
- Co-evolve the MET Group AI strategy with CIO and stakeholders
- Stay up-to-date with market developments, costs, and make/buy considerations
- Work with delivery squads to diagnose time-costing engineering practices or manual work
- Design agentic, machine learning, or automated solutions
- Increase quality, security, and value delivery using agentic loops in CI/CD pipelines
- Implement observability practices and evals
- Drive quality and standardization in MLOps
- Manage use of a central feature store, training data sets, eval data sets, and model drift eval approach/tooling
- Bring the organization and employees along in their AI adoption journey
- Listen, coach, and design AI practice adoption
- Measure adoption of AI practices
Berufserfahrung
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Tools & Technologien
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Zur Originalanzeige bei MET GroupÜber das Unternehmen
The company consolidates and develops international natural gas sales and purchase portfolio and establishes a trading arm for commodity lines.
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