AI Engineering - Databricks

Build production-grade AI systems on Databricks, working across the full data and ML lifecycle in a team doing serious engineering work.

You'll design, build, and maintain AI and machine learning solutions on the Databricks platform. That means working across data pipelines, model development, and deployment, turning raw data into systems that actually run in production. You'll work closely with data engineers and analysts to make sure models don't just get built, they get used.

The environment is built around modern tooling. Databricks is the core platform, so you'll spend your time in Delta Lake, MLflow, and the Lakehouse architecture rather than stitching together half a dozen disconnected tools. If you've got opinions about how ML systems should be built and maintained, there's room to apply them here.

This is the kind of role where you own your work end to end. From feature engineering through to model monitoring in production, you're across the whole lifecycle. The team values engineers who understand the data side as well as the modelling side, so you won't be siloed into one narrow function. If you want to grow across both data engineering and AI, this is a good place to do it.

What You'll Do

• Design and build ML pipelines on Databricks, from ingestion and feature engineering through to model training and deployment.
• Monitor and maintain models in production, including performance tracking, retraining workflows, and data quality checks.
• Work with data engineers and business teams to translate requirements into scalable AI solutions.
• Contribute to best practices around ML architecture, code quality, and platform governance within the Databricks environment.

What You'll Need

• Hands-on experience with Databricks, including Delta Lake, MLflow, and Spark-based processing.
• Strong Python skills and experience building and deploying machine learning models in a production setting.
• Solid understanding of data engineering concepts, including pipeline design, data modelling, and orchestration tools.
• Experience with cloud platforms (Azure, AWS, or GCP) and how Databricks sits within a broader data architecture.

About the Company

They're an organisation using AI and data engineering to solve real business problems at scale. The team is focused on doing the work properly, with modern tooling, clear ownership, and a genuine investment in building things that last.

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