Machine Learning Engineer - Energy Management

Details

Build production-grade machine learning solutions using Databricks, MLflow, AWS and modern MLOps practices. Own the complete machine learning lifecycle, from scalable data processing and model training to production deployment and monitoring. Collaborate in a multidisciplinary engineering team where Machine Learning Engineers, Data Scientists and Data Engineers build reliable, scalable AI solutions together.

Responsibilities

• Design, build and maintain end-to-end machine learning pipelines for training, validation and production inference in both batch and real-time environments.
• Develop scalable MLOps capabilities, including CI/CD pipelines, automated testing, model versioning, deployment strategies, drift detection and rollback mechanisms.
• Build and operate Databricks-based data processing and machine learning workflows at scale.
• Manage MLflow for experiment tracking, model registry and reproducible machine learning deployments.
• Deploy, operate and optimize machine learning workloads on AWS while balancing performance, scalability and cost.
• Develop scalable feature engineering pipelines, data contracts and feature stores, including Scala-based ETL pipelines where applicable.
• Build monitoring, alerting and observability for production models, including model performance, latency, throughput, and data and model drift.
• Work closely with Data Scientists to productionize machine learning models, provide feedback on production constraints and continuously improve deployed solutions.
• Perform post-deployment analyses to evaluate and improve model performance and operational reliability.
• Contribute to platform improvements, automation and MLOps best practices across the team.
• Mentor fellow engineers and contribute to a strong engineering culture.

Requirements

• 4+ years professional experience in ML engineering; 2+ years owning MLOps/production ML systems.
• Hands-on experience with Databricks (notebooks, jobs, clusters) for large-scale data processing and model training.
• Production experience with MLflow for experiment tracking and model registry.
• Strong AWS experience deploying and operating ML workloads.
• Proficient in Scala for data processing/ETL on Spark; production-quality code and familiarity with the JVM ecosystem.
• Experience building CI/CD for ML and containerized deployments (Docker, Kubernetes).
• Practical knowledge of model monitoring, A/B testing, canary deployments, and data/model drift detection.
• Strong software engineering fundamentals: testing, design patterns, code reviews, and documentation.
• Excellent communication and collaboration skills.

Where you'll work

You will join a multidisciplinary team of Machine Learning Engineers, Data Scientists, Data Engineers and Data Analysts within the Energy Management Systems domain. Together you develop, deploy and operate machine learning solutions that support Eneco's digital products and services. The role is based in Rotterdam and follows a hybrid working model (40% office, 40% home, 20% flexible). With manager approval, you may work abroad (within approved countries) up to 3 weeks/year, max 2 consecutively.

What we have to offer

• Gross annual salary between €84,000 and €117,000 (including FlexBudget, 8% holiday allowance, and depending on role a bonus or collective profit sharing).
• FlexBudget: can be paid out, used to buy extra holiday days or saved.
• Personal and professional growth support.
• Hybrid working: home, office or abroad as described above.

Application & Contact

For more information about the job function or application procedure, contact the recruiter (Venetia de Wit).