Machine Learning Engineer - Energy Management

Eneco; Rotterdam; On-site; EUR 90,000 - 130,000; Full time

Job summary

Eneco is building reliable, production‑grade machine learning systems within the Energy Management Systems domain in Rotterdam. The role focuses on delivering scalable ML pipelines, MLOps tooling and platform improvements across Databricks, MLflow and AWS.

You will work with Data Scientists, Data Engineers and Software Engineers to productionize models, monitor performance and continuously improve deployed solutions, while mentoring peers and contributing to a culture of collaboration and

Qualifications

• 4+ years of professional ML engineering experience with a focus on production systems.
• 2+ years owning MLOps or production ML platforms.
• Hands-on Databricks for large-scale data processing and model training.
• Production ML with MLflow for experiment tracking and model registry.
• Strong AWS experience deploying and operating ML workloads.
• Proficiency in Scala for data processing and Spark-based ETL; JVM familiarity.
• Experience building CI/CD for ML and containerized deployments (Docker/Kubernetes).
• Knowledge of model monitoring, A/B testing and drift detection.
• Solid software engineering fundamentals with strong documentation and tests.
• Excellent communication and collaboration in multidisciplinary teams.

Responsibilities

• Design, build and maintain end-to-end ML pipelines for training, validation and production inference.
• Develop scalable MLOps capabilities, including CI/CD, testing, versioning and rollback strategies.
• Build and operate Databricks-based data processing and ML workflows at scale.
• Manage MLflow for experiment tracking, model registry and reproducible deployments.
• Deploy, operate and optimize ML workloads on AWS focusing on performance and cost.
• Develop scalable feature pipelines and feature stores, with scalable Scala-based ETL where applicable.
• Build monitoring, alerting and observability for production models and data drift.
• Collaborate with Data Scientists to productionize models and improve constraints.
• Perform post-deployment analyses to evaluate model performance and reliability.
• Contribute to platform improvements, automation and MLOps best practices.
• Mentor fellow engineers and foster a strong engineering culture.

Skills

ML engineering

MLOps

Databricks

MLflow

AWS

Scala

Spark

Docker

Kubernetes

CI/CD

Monitoring

Software engineering

Communication

Tools

Databricks

MLflow

AWS

Scala

Docker

Kubernetes

Spark

JVM

Job description

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.

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 recruiter at: [e-mail]

• 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.