Machine Learning Engineer - Energy Management

At Eneco, we are committed to accelerating the energy transition. Through our One Planet strategy, we aim to become climate neutral by 2035 - together with our customers.

Digital technology and data play a key role in achieving this ambition. Within the Energy Management Systems domain, we develop reliable, scalable and intelligent data-driven solutions that support Eneco's digital products and services. As a Machine Learning Engineer, you will help bring machine learning solutions into production by building the platforms, tooling and engineering practices that enable models to deliver reliable business value.

Working closely with Data Scientists, Data Engineers and Software Engineers, you will build and operate production-grade machine learning solutions while helping shape the future of MLOps within Eneco.

• 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.

• Build, deploy, and maintain end-to-end ML pipelines for training, validation, and production inference (batch and real-time)
• Design and implement robust MLOps practices: CI/CD for models, model versioning, automated testing, drift detection, and rollback strategies.
• Implement and operate Databricks-based data processing and model training workflows at scale.
• Integrate and manage MLflow for experiment tracking, model registry, and reproducible runs.
• Design, deploy, and optimize ML workloads on AWS with cost and performance tradeoffs.
• Develop and maintain data contracts, feature stores, and scalable feature engineering pipelines (including Scala-based ETL where applicable).
• Build monitoring, alerting, and observability for model performance, latency, throughput, and data/label drift.
• Collaborate with data scientists to productionize models, provide feedback on model design for production constraints, and run post-deployment analyses.
• Mentor engineers on MLOps best practices and contribute to team-wide tooling and automation.

You are passionate about building reliable production systems and enjoy bringing machine learning solutions into production. You combine strong software engineering skills with hands-on MLOps experience and thrive in multidisciplinary teams where engineering and data science come together.

• 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 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.

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.

Our team values collaboration, ownership and continuous learning. We encourage experimentation, knowledge sharing and continuous improvement while building reliable, scalable machine learning solutions that contribute to Eneco's ambition to accelerate the energy transition.

Contact our recuiter at: [email protected]

• 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.