Machine Learning Engineer

Within Business Area Markets , business decisions are made around the clock and at high frequency, creating strong opportunities for intelligent automation and applied machine learning. As an ML Engineer in our Data Science team, you will design, build, and evolve machine‑learning systems that run in production and directly support real‑time electricity trading on the energy markets. Your work has immediate, measurable impact on trading decisions, asset optimization, and business outcomes. What you will do In this role, you will actively shape how our ML systems and platform evolve over time, rather than following a fixed blueprint. Own and develop production ML systems that support real‑time trading and operational decision‑making Design and improve MLOps pipelines, including training, deployment, retraining, and monitoring Build and operate real‑time inference services, ensuring alignment between batch training and streaming inference Contribute to ML and data architecture decisions, including trade‑offs between batch and streaming processing and long‑term maintainability Collaborate closely with data scientists, traders, and engineers to translate business needs into robust, scalable ML solutions

What You Bring  The position is open to both mid‑level and senior engineers. What matters most to us is drive, creativity, and the motivation to take ownership of impactful technical systems. If you want to influence architecture, challenge existing solutions, and see your work make a real difference, you’ll fit in well.  Experience working with production ML systems in Python, depth can vary by seniority.  A solid understanding of ML system lifecycle challenges such as deployment, retraining, drift, and reproducibility.  Interest in or experience with MLOps concepts and automated pipelines.  Familiarity with distributed or containerized systems (Docker, Kubernetes), or strong motivation to deepen this skillset.  Most importantly: drive, curiosity, and creativity, with a mindset of ownership and a willingness to challenge and improve existing technical choices.  Degree in Computer Science, Engineering, Data Science, or similar, as well as fluency in English  Growth & Development  This role offers a clear path to grow beyond implementation: Influence ML and data architecture decisions across BA Markets.  Develop toward a technical lead or ML architect role over time.  Deepen expertise in large‑scale, streaming, and real‑time ML systems used in a critical business domain.