Machine Learning Engineer

As a Machine Learning Engineer in the ML Platform team, you will design and build the foundational services, tooling, and infrastructure that enable Machine Learning I use cases across Eneco. 

You will work at the intersection of Machine Learning, Software Engineering, and Cloud Infrastructure, with a strong focus on Databricks-based platforms. Rather than delivering a single model or product, your work will empower multiple teams to develop, deploy, and operate ML solutions efficiently and safely. You will collaborate closely with data scientists, product teams, and other platform engineers to turn ML capabilities into reusable, production-ready services. 

Must Have: 

• Hands-on experience with Apache Spark and Databricks — including building/optimizing distributed pipelines for feature engineering, training, and batch/stream inference 

• Strong experience in Machine Learning or applied data science 

• Solid Python programming skills and experience with ML frameworks such as scikit-learn, PyTorch, or similar 

• Experience using Docker, Kubernetes, or MLflow for model lifecycle management. 

• Understanding of software engineering principles: testing, CI/CD, version control, containerization. 

• Strong communication and mentoring skills, specifically being able to explain complex technical concepts in a tangible way 

Nice to Have: 

• Experience building shared ML platform components used by multiple teams (templates, libraries) 

• Feature engineering patterns such as feature stores and offline/online consistency 

• Familiar with infrastructure as code (IAC) such as Terraform or Databricks Assets Bundle (DAB) 

• Experience working in a platform team / Ability to design systems that support multiple teams and use cases. 

• Experience with building and maintaining reusable ML / MLOps tooling. 

• Knowledge about open-source table formats such as Delta and Iceberg 

• Designing a framework for end-to-end ML pipelines, including ingestion and processing of structured and unstructured data. 

• Building and maintaining shared ML platform components, libraries, and templates used across teams. 

• Monitoring model performance, data quality, drift, latency, and cost in production. 

• Implementing MLOps practices, including model versioning, CI/CD, monitoring, and retraining workflows. 

• Testing the newest Databricks features in a PoC setting and assessing viability for large scale implementation within Eneco. 

• Collaborate with cross-functional teams (product, engineering, data science) to bring Machine Learning-powered features to life. 

• Guiding and mentoring Data Scientists in bringing their ML solutions to a production grade level.