Databricks MLOPS

Role Title: Databricks MLOPS Engineer Contract\

Sector: Energy

Location: Melbourne (Hybrid 3 days per week) A global leader in low-carbon energy and services committed to accelerating the transition towards a carbon-neutral economy. With 97,000 employees worldwide, we prioritize economic performance alongside positive impacts on people and the planet. Our expertise spans gas, renewable energy, and services, enabling us to deliver competitive and sustainable solutions.

Productionize machine learning (ML) workloads end-to-end on the Databricks platform. Build and maintain automated ML pipelines, including training and inference workflows. Collaborate with pipeline development and orchestration teams to integrate ML workflows following established best practices and guidelines.

Manage the full ML model lifecycle utilizing MLflow for experiment tracking, versioning, and model registration, ensuring reproducibility from development through to production. Implement CI/CD processes to automate testing and deployment of model code. Monitor production models for drift, performance degradation, and data quality using model serving and observability tools.

Apply data and model governance using Unity Catalog for permissions management, data lineage, and model lineage at scale. Work closely with global forecasting and analytics teams to support migration activities and enhance current systems.

Proven hands-on experience with the Databricks platform and MLOps engineering. Strong skills in database management and pipeline development, including pipeline orchestration and ML workflow integration. Familiarity with MLflow and Databricks core tools such as Unity Catalog, Feature Store, and Model Serving is highly desirable.

AWS experience is advantageous, particularly related to data lakes and cloud migration projects. Ability to follow and implement best practices and governance standards in MLOps environments.

Demonstrated track record of productionizing ML workloads on Databricks without building ML models themselves. Experience working with data lakes and undertaking AWS migration activities is preferred. Previous engagement with large-scale enterprise energy sector data engineering and cloud infrastructure is a plus.