Machine Learning Engineer - Energy Management

Details

Build production-grade machine learning solutions that support Eneco's digital products and services and help shape the future of MLOps within the Energy Management Systems domain. You will work with Machine Learning Engineers, Data Scientists, Data Engineers and Data Analysts to develop, deploy and operate reliable, scalable AI solutions.

Responsibilities

• Design, build and maintain end-to-end machine learning pipelines for training, validation and production inference in 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, 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

• Four or more years of professional experience in machine learning engineering, including two or more years owning MLOps or production ML systems.
• Hands-on experience with Databricks notebooks, jobs and 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 machine learning workloads.
• Proficiency in Scala for data processing and ETL on Spark, with production-quality coding skills and familiarity with the JVM ecosystem.
• Experience building CI/CD for machine learning and containerized deployments using Docker and Kubernetes.
• Practical knowledge of model monitoring, A/B testing, canary deployments, and data and model drift detection.
• Strong software engineering fundamentals, including testing, design patterns, code reviews and documentation.
• Excellent communication and collaboration skills.

Work Environment

You will join a multidisciplinary team within the Energy Management Systems domain. The team values collaboration, ownership, continuous learning, experimentation, knowledge sharing and continuous improvement.

The role uses a hybrid working model: 40% at the office, 40% from home and 20% flexibly. With manager approval, working abroad within approved countries is permitted for up to three weeks per year, with a maximum of two consecutive weeks.

Benefits

• Gross annual salary between €84,000 and €117,000, including FlexBudget and 8% holiday allowance; depending on the role, a bonus or collective profit sharing may also apply.
• FlexBudget that can be paid out, used to purchase additional holiday days or saved.
• Personal and professional development opportunities.
• Flexible working hours and the option to work from home where applicable.
• Inclusive work environment and support for workplace adjustments for people with physical or sensory disabilities.