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

E.ON Drive Infrastructure

2d•Hybrid

About The Position

E.ON Drive Infrastructure (EDRI) is hiring a Senior AI/ML Engineer for its newly established AI UNIT, a special unit built and run like a startup. The role designs, builds, and scales AI systems for physical infrastructure across a pan-European electric-vehicle charging network. The engineer works across the AI lifecycle, taking solutions from experimentation into production and owning them there, while helping define the AI UNIT’s technical foundation. This is a senior individual-contributor role that also supports the growth of junior colleagues. The position reports to the Head of AI Strategy. EDRI is a pan-European charge point operator owned by the E.ON group. It builds, owns, and operates public charging infrastructure for electric vehicles. The team has more than 200 employees and operates over 9,000 public charging points across 11 European countries, with plans to install 1,000 additional high-power charging points per year in partnership with locations. The position offers hybrid working and flexible working hours, as well as the option to work temporarily from abroad within the EU. Benefits include a competitive salary, a company pension scheme, and a training budget of €5,000 per year. The company provides onboarding with welcome gifts and the hardware needed to get started.

Requirements

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 translate complex business and technical challenges into practical solutions.
Curious, pragmatic, and passionate about applying the latest AI technologies to real-world problems.
Strong communication skills and ability to collaborate effectively across technical and non-technical teams.

Nice To Haves

Fine-tuning, post-training, or inference optimization experience.
Optimization or operations-research experience.
Experience with physical infrastructure, energy, mobility, or other asset-heavy industries.
Open-source contributions or public work; side projects count.

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; own their reliability, quality, latency, and cost.
Develop classical machine-learning and generative-AI solutions, including RAG, agentic systems, and AI-powered services.
Productionize machine-learning models developed by Data Scientists and operate them at scale.
Define and enhance MLOps and LLMOps practices for deployment, versioning, monitoring, evaluation, and retraining.
Own the LLM gateway, including model routing, token limits, cost per use case, guardrails, and logging.
Build scalable AI services and APIs for Full-Stack and AI Deployment Engineers.
Evaluate emerging AI models, AI tooling, and agentic systems, and integrate impactful innovations into the technology stack.
Share knowledge and support the growth of junior colleagues.