Role Overview
As a Machine Learning Operations (MLOps) Engineer, you play a pivotal role in bridging AI, machine learning, software engineering, and 24/7 grid operations. You drive the transition from a traditional "push" IT model to a "pull" operational ownership model, ensuring that machine learning and data-driven solutions supporting high-voltage grid operators are reliable, scalable, and fully manageable in a mission-critical environment.
Key Responsibilities:
- MLOps Implementation: Design, build, and optimize MLOps pipelines and processes for model deployment, version control, validation, lifecycle management, and monitoring.
- Cross-Functional Collaboration: Partner with data scientists, software engineers, architects, IT specialists, and grid operators to integrate data innovations seamlessly into day-to-day business operations.
- Standardization & Governance: Establish and refine management standards, governance frameworks, and incident management protocols for AI models and data products.
- Operational Bridge & Translation: Translate operational requirements from 24/7 grid controllers into technical use cases, helping the business build internal AI and data literacy.
Your Deliverables:
- Robust, scalable MLOps architecture and CI/CD pipelines within the Grid Security domain.
- Standardized processes for machine learning model versioning, monitoring, and lifecycle management.
- Integrated, mission-critical applications assisting 24/7 power grid operators.
Your Profile:
- Education (Hard Requirement): Completed Bachelor's (HBO) or Master's (WO) degree in Data Science, Computer Science, Artificial Intelligence, Software Engineering, or a related field.
- Minimum of 3 years of relevant work experience in MLOps, Machine Learning Operations, Data Engineering, or equivalent disciplines.
- Proven background in CI/CD processes, software lifecycle management, and the automation/monitoring of mission-critical applications.
- Demonstrated experience bridging business users, IT, and data science teams.
- Language: Good command of both Dutch and English (written and spoken).
- Preferred Skills & Background: Cloud platforms (Azure, AWS, GCP); containerization (Docker, Kubernetes); model management tools; observability/monitoring solutions; experience within energy/utilities or 24/7 operational environments.
