University degree in an engineering or analytical field (Computer Science, Mathematics, Physics, Statistics, Engineering or a related discipline)
5+ years of engineering experience, with significant time spent supporting or building ML systems in production
Proficiency in Python and software engineering best practices: testing, code quality, code review, CI/CD, monitoring, and modular code design
Solid working knowledge of MLOps practices: pipeline setup, deployment, monitoring
Enough fluency in ML/statistical modeling to collaborate effectively with data scientists and make sound architectural tradeoffs together
Independent, pragmatic problem-solving with strong attention to detail in a fast-paced environment
Excellent English communication and interpersonal skills
Cross-functional collaboration mindset across data scientists, software engineers, and partner-company stakeholders

Experience in energy, power markets, or other near-real-time operational domains
Familiarity with orchestration tools (Airflow or similar), MLOps toolchains (MLflow, Sagemaker, or similar), and streaming systems (Kafka or similar)
Hands-on experience with large-scale data tooling: Spark, Dask, or comparable frameworks
Experience designing or owning near-real-time analytics and/or ML workflows, including observability
Track record of taking models from research into production on AWS or comparable cloud provider

You don’t have the habit of defining your own tasks and have a preference for working in clearly separated functions

Senior MLOps Engineer (m/w/d)