Location: Munich, Germany Employment Type: Full-Time, indefinite term Salary Range: € 95,000 – 115,000 per year gross, depending on seniority level Office First work setup Language Requirement: C1 Level English Act as the technical bridge between data science and software engineering, helping research models become reliable, maintainable production systems, and helping engineering understand what ML workloads actually need Design and build the data and feature pipelines that support Flexa's forecasting and trading models at scale across hundreds of thousands of distributed systems Leverage Flexa’s deployment, orchestration, and serving platform to bring models into production, for both batch and real-time workloads Establish monitoring and observability for models in production, like drift, data quality, latency, and failure modes Partner closely with data scientists on model design and validation, bringing an engineering perspective on scalability, maintainability, and production risk from early on Champion engineering rigor and ML best practices to foster an open, data-driven engineering culture. Contribute to the technical roadmap, anticipating scaling needs as data volume and model complexity grow Opportunity to guide and develop more junior colleagues through design review, code review, and structured feedback Be part of a cross-functional team of data scientists, software engineers, and other teams across Flexa's partner ecosystem Mandatory Requirements 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 Skills to Set You Apart 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 This won’t be the right role for you if… You don’t have the habit of defining your own tasks and have a preference for working in clearly separated functions