Senior AI/ML Engineer (m/f/d)

Einblick in das Angebot

Your missionMost AI lives in a chat window. Ours will run physical infrastructure across a pan-European network. As a Senior AI/ML Engineer in our newly established AI UNIT within E.ON Drive Infrastructure (EDRI), a special unit built and run like a startup, you will be at the forefront of designing, building, and scaling AI systems that deliver real business impact. Working across the entire AI lifecycle, you will take solutions from experimentation to production and own them there, while helping define the technical foundation of our AI UNIT. You report to our Head of AI Strategy.

Your Responsibilities 
Shape the AI/ML architecture, technology stack, and engineering standards of the unit
Design, build, and operate production-grade AI and machine learning systems and own their reliability, quality, latency, and cost
Develop classical ML and GenAI solutions, including RAG, agentic systems, and AI-powered services
Productionize ML models developed by our Data Scientists and operate them at scale
Define and enhance MLOps and LLMOps practices for deployment, versioning, monitoring, evaluation, and retraining
Own our LLM gateway, including model routing, token limits, cost per use case, guardrails, and logging
Build scalable AI services and APIs for our Full-Stack and AI Deployment Engineers
Evaluate emerging AI models, AI tooling, and agentic systems and integrate impactful innovations into our stack
Share knowledge and support the growth of junior colleagues – this is a senior individual contributor role

Your profileYou are an experienced AI engineer who combines strong software engineering fundamentals with hands-on expertise in modern machine learning and Generative AI. You enjoy solving complex problems, taking ownership, and turning ideas into production-ready solutions. 

Must have 
Proven experience owning AI/ML solutions in production end to end, with measurable impact
Strong software engineering skills in Python, including testing, CI/CD, and clean architecture
Solid understanding of machine learning concepts, model evaluation, and experimentation
Production experience with Generative AI, including prompting, RAG, agents, and evaluation frameworks
Experience with MLOps and model deployment workflows in Azure – AWS or GCP experience transfers
Strong understanding of trade-offs between accuracy, latency, scalability, cost, and risk
Ability to translat