Senior Machine Learning Engineer

Own the journey from ML experimentation to production - Turn forecasting and other ML models into reliable, scalable, reproducible systems that directly support trading, pricing, and customer insights. Shape ML engineering standards and architecture - Drive technical decisions, establish teamwide engineering standards, and mentor engineers in a highly technical, production-first environment. Build ML for a complex, real-world energy environment - Develop robust pipelines, backtesting frameworks, and monitoring capabilities that handle dynamic markets and incomplete or delayed data while supporting Eneco’s energy-transition ambitions.

You will work closely with data scientists to experiment with and operationalize ML models for various uses such as demand forecasting, asset detection, and market simulations. Together with data engineers, you will build the foundations that enable reliable deployment of these models and monitoring in production environments. Your focus is turning research into robust production systems while ensuring reproducibility, validation, and observability across the ML lifecycle.

ML Model Experimentation Work with and enable data scientists to run experiments with ML models. Design, build, and maintain backtesting and experimentation frameworks. Ensure reproducibility across research, experimentation, and live execution environments. Data & Feature Engineering Build and maintain data & feature pipelines used by ML models. Collaborate closely with data engineers on optimizing the structure and performance of underlying data models. Write technical documentation and encode explicit dependencies to enable clear data lineage & governance. Handle incomplete, delayed, or partial data safely in both experimentation and production environments. Productionization & Platform Integration Support deployment, versioning, rollback, and release management of ML models into production-grade pipelines. Optimize runtime performance and resource utilization where relevant. Implement validation, safeguards, and operational controls before production deployment. Ensure stable and deterministic execution alongside other engineers. Monitoring, Reliability & Risk Awareness Implement monitoring and observability for forecasting models. Support drift detection, anomaly monitoring, and performance degradation analysis. Resolve operational data incidents and conduct post-mortems. Ensure deterministic reruns and explainability of historical models.

Must have Strong software engineering skills in Python. Proven experience productionizing, maintaining, and monitoring ML models and pipelines. Hands-on experience with distributed data and compute platforms such as Databricks or similar technologies. Experience deploying workloads into containerized or service-based execution environments. Experience designing or maintaining backtesting and simulation frameworks. Strong understanding of reproducibility, validation, and reliability within time-series or event-driven systems. Strong ownership mentality with a production-first engineering mindset. Experience setting teamwide engineering standards and driving architectural decisions. Experience mentoring engineers and providing constructive review of technical designs and code. Nice to have Experience with demand forecasting, energy markets, and asset detection. Experience with cloud-native architectures and scalable distributed systems. Experience collaborating closely with data scientists and data engineers. Experience with dbt, Snowflake, and Java

You’ll become part of a team focused on enabling accurate and efficient demand forecasting, ML experimentation, and key insights on customer behavior and trends. The team combines expertise across data science, data engineering, data analytics, and ML engineering.