Senior MLOps Engineer
Sytac - Openings · Rotterdam ·
- Seniority
- Senior
- Employment
- Contract
- Category
- Devops
- Experience
- 5+ years
Sytac - Openings · Rotterdam ·
The energy transition is in full motion, and data plays an essential role in enabling it. Our client manages vast amounts of data and is experiencing rapid growth in the number of data science use cases. To fully support their data scientists, they are building a modern MLOps platform within Azure. A new MLOps environment (based on Azure Databricks) has recently been set up, and further expansion is underway. To strengthen their MLOps capability and support ongoing use cases, our client is seeking temporary reinforcement in the form of a Senior MLOps Engineer.
In this role, you will bring your expertise to design, build, and improve the MLOps infrastructure. Your initial focus will be on developing and rolling out the internal MLOps platform: setting up automated pipelines, model registration, and deployment workflows in Azure Databricks, including the use of MLflow for model management. You will ensure that data science models can be deployed to production more efficiently and reliably. Examples include large-scale time-series forecasting workflows running on Databricks Jobs with MLflow, scheduled via Apache Airflow; you ensure that these pipelines run smoothly. You will also introduce structure and best practices for CI/CD in machine learning (via Azure DevOps) to ensure proper versioning of code, models, and data.
As the assignment progresses, your focus will shift toward knowledge transfer and coaching the existing engineers. You help the team grow, mature, and become self-sufficient, ensuring that the improvements you introduce are embedded for the long term. You leave the team stronger than you found it.
Candidate is available from 05/01/2026 to 04/01/2027 for 40 hours per week, with possible extension
Completed HBO/WO (Bachelor's/Master's) degree
Experience as a tech lead, capable of guiding and enabling a team
Experience designing and improving MLOps infrastructure within a large, complex organization
At least 5 years of professional experience in machine learning product development within an MLOps context from data preparation and model training to deployment and monitoring
Excellent Python skills and solid software design principles (OOP, design patterns)
Demonstrable experience building and maintaining an MLOps or data platform, ideally in Azure, and preferably with Databricks
Understanding of what it takes to deploy ML solutions at scale reliably and securely
Knowledge of Terraform or Infrastructure-as-Code
Experience with the current tech stack is a plus: Apache Spark, Azure Databricks, MLflow, Apache Airflow, Azure DevOps, data lineage / versioning
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