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# Machine Learning Engineer

- **Company:** PureGym Limited
- **Location:** London, UK
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Automation of Tests, Microsoft Azure, Code Review, Data Warehousing, Github, Python (Programming Language), Machine Learning, Release Management, Standard Sql, Software Engineering, Management of Software Versions, Delivery Pipeline, Pyspark, Machine Learning Operations, Software Version Control, Databricks
- **Published:** October 7, 2026
- **Apply:** https://www.jobs.service.gov.uk/jobs/6ac3e5f5d469a339a41daba0/apply

## About the Role

The most important thing is that you've successfully taken machine learning models into production and understand what it takes to keep them running reliably., * Strong Python and SQL skills, combined with good software engineering practices including testing, packaging, code review and version control.

 * Experience with AI assisted tooling
 * Hands-on experience taking machine learning models into production and supporting them once live.
 * Experience with MLflow or similar model lifecycle tooling, including deploying models for batch and/or near real-time use cases.
 * Experience building or working with CI/CD pipelines, for example GitHub Actions or Azure DevOps.
 * The ability to communicate clearly with both technical and non-technical colleagues.
 * A degree in Computing, Engineering, a quantitative discipline or similar or equivalent practical experience

Advantageous skills you may bring:

 * Experience working with Databricks, ideally including Unity Catalog, Jobs/Workflows and PySpark.
 * An understanding of infrastructure as code and reproducible deployment practices.
 * Familiarity with dbt and modern data warehouse/lakehouse patterns.

## Description

You'll be the engineer who takes our machine learning models from notebook to reliable, scalable production services, and keeps them performing once they're live.

This is a hands-on role with genuine ownership. You'll own deployment, monitoring and retraining across our model estate, while building the reusable components, tooling and standards that make every new model faster, safer and more cost-effective to ship than the last.

Working closely with our Data Scientists and Data Engineers, you'll help shape how we do MLOps at PureGym, bringing strong software engineering practices into the modelling lifecycle and making it easier for the team to get great models into production.

What you'll be doing:

 * Own the path to production for our machine learning models, including packaging, deployment, release management and rollback using Databricks and MLflow.
 * Build robust CI/CD pipelines for models and data science code, introducing automated testing, environments and appropriate release gates.
 * Build monitoring for data drift, model performance and pipeline health, with clear alerting and runbooks so issues can be identified and resolved quickly.
 * Automate recurring model runs, retraining and scoring that are currently manual, freeing our Data Scientists to focus on new modelling and experimentation.
 * Turn models and metric definitions created by Data Scientists into robust, reusable production components on our shared semantic layer.
 * Help define and evolve our MLOps standards, covering versioning, model registry practices, documentation and handover.
 * Partner with Data Engineers on upstream pipelines and Data Scientists on model design, ensuring production requirements are considered from the outset.
 * Take an active role in code reviews and coach Data Scientists in software engineering best practice.
 * Monitor the cost and performance of our model estate and identify opportunities to make it more efficient.
 * Help shape how machine learning engineering is done at PureGym, creating patterns and standards that can scale as our use of ML grows.

The Person

You're an engineer who enjoys the challenge of making machine learning work reliably in the real world.

You don't just want to deploy a model and move on. You care about what happens afterwards: how it performs, how it's monitored, how easily it can be changed and how confidently the next model can be taken into production.