Overview
A leading organisation is looking for a Databricks-focused MLOps Engineer to take ownership of a dedicated ML Engineering environment, supporting a growing data science team and accelerating the route from model development into production.
The business operates across a modern data landscape including both Palantir and Databricks, with some cross-platform integration expected as the environment develops. This role will focus specifically on the Databricks MLOps setup, ensuring it is performant, scalable, secure, well-governed, and able to support production ML products in a structured and repeatable way.
The Role
You will manage and improve the Databricks environments used by a team of 8 data scientists, with the team growing quickly as demand for ML products increases. The main focus is improving how models are deployed, monitored, governed, and supported in production.
This is a delivery-focused role with a strategic element. The client needs someone who can understand the Databricks roadmap, advise on what the business should adopt, and turn that into practical MLOps frameworks, deployment patterns, and operating processes.
You will help bring the target operating model to life, create a clear path-to-production, and support the internal ML Engineering capability while the permanent team continues to grow.
Key Responsibilities
- Own and manage dedicated Databricks environments supporting ML Engineering and MLOps
- Ensure the platform is performant, scalable, secure, and well-governed
- Support a growing team of data scientists in operationalising, deploying, and managing their models
- Build out reusable MLOps frameworks, standards, and deployment patterns
- Improve the path from model development through to production
- Support model observability, monitoring, governance, and operational controls
- Work closely with Databricks to understand their roadmap and advise on relevant adoption
- Help bring the full MLOps operating model and solution design to life
- Support the development of internal ML Engineering capability
- Work across Databricks, Palantir, data science, and engineering teams where required
- Ensure ML products and services can be delivered in a structured, repeatable, and scalable way
Key Skills and Experience
- Strong experience with Databricks in a production ML, MLOps, or data platform environment
- Experience working across MLOps, ML Engineering, or ML Platform Engineering
- Strong understanding of model deployment, model monitoring, CI/CD, versioning, and ML lifecycle management
- Experience building frameworks, standards, and reusable patterns for production ML delivery
- Experience supporting data scientists and helping move models into production
- Strong Python and PySpark experience
- Experience with cloud data platforms, ideally Azure
- Strong understanding of scalable and governed ML platform environments
- Ability to operate strategically while remaining hands-on and delivery-focused
- Strong stakeholder management skills across technical and non-technical teams
Nice to Have
- Palantir experience or exposure to cross-platform data environments
- Unity Catalog, Delta Lake, MLflow, Feature Store, or Model Registry experience
- Experience building out ML Engineering capability or MLOps functions
- Experience in enterprise or regulated environments
- Vendor roadmap or platform strategy experience
- Responsible AI, model governance, or risk management experience
- Cloud certifications or Databricks certifications