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MLOps Engineer 

Start Date: ASAP

End Date: 31/12/2026 (with possibility of extension)

Work Regime: Full-time, 2 to 3 days on-site

Role Overview:

The position of MLOps Engineer is primarily focused on platform engineering within the domain of machine learning. This is not a data scientist role; you will not be designing models or interpreting statistical results. Instead, your focus will be on developing automated systems that transition a data scientist’s code from a pull request to a production-trained, monitored, and self-healing model. Your experience and ideas will be valued as we iteratively build this platform.

Key Responsibilities:

• Develop and maintain ML pipelines, including training, evaluation, registration, and deployment using Databricks Asset Bundles, Lakeflow Jobs, and MLflow.
• Manage CI/CD for ML, ensuring every pull request triggers a training run, emits metrics, and reports results.
• Design environment boundaries for development, staging, and production, ensuring code and models are appropriately promoted.
• Build the observability layer to provide data scientists with visibility into production data distributions and model metrics.
• Contribute to the transition to Azure Kubernetes Service (AKS) for online model serving, collaborating with platform and DevOps engineers.
• Implement monitoring and retraining triggers to detect data and prediction drift, ensuring timely retraining.
• Define infrastructure as code using Terraform or Asset Bundles, ensuring all components are reviewed and deployed via pipelines.

Essential Qualifications:

• Strong foundation in Python engineering, with an emphasis on writing testable and maintainable code.
• Hands-on experience with Databricks, including MLflow experiment tracking, Jobs/Lakeflow, and Unity Catalog.
• Experience in designing and operating CI/CD pipelines for complex workloads using tools like GitHub Actions or Azure DevOps.
• Familiarity with Docker and container-based deployment.
• Understanding of ML pipeline stages, including data validation, feature engineering, training, evaluation, model registration, serving, and monitoring.

Desirable Skills:

• Experience with Kubernetes / AKS, including deploying containerized workloads and writing Helm charts or Kustomize configs.
• Proficiency with Terraform for infrastructure provisioning on Azure.
• Familiarity with MLflow Model Registry or Unity Catalog model management workflows.
• Exposure to data drift or model monitoring tools such as Databricks Lakehouse Monitoring, Evidently AI, or Whylogs.
• Familiarity with Databricks Asset Bundles (DABs) for defining ML resources as code.

Additional Considerations:

A background in ML or data science is beneficial, as you will work closely with data scientists, though model training is not required in this role.