Machine Learning Engineer

Overview

In this role you will operationalise machine learning models from notebooks into reliable production services. You will own deployment, monitoring and retraining, while building reusable tooling and standards to scale ML work. You will collaborate with Data Scientists and Data Engineers to shape MLOps practices and improve how models are brought to production at PureGym. This is a hands-on, ownership-driven position in a supportive, data-enabled environment with a focus on reliability and cost-effectiveness.

Pay / Benefits

• Free nationwide gym membership for you + 1
• Hybrid working
• Private healthcare
• Life insurance x4
• Company pension contribution
• 25 days annual leave + 1 personal day

Responsibilities

• Own the path to production for ML models, including packaging, deployment, release management and rollback using Databricks and MLflow
• Build robust CI/CD pipelines for models and data science code with automated testing and release gates
• Monitor data drift, model performance and pipeline health with clear alerting and runbooks
• Automate recurring model runs, retraining and scoring to free Data Scientists for experimentation
• Translate models and metric definitions into robust, reusable production components on a shared semantic layer
• Help define and evolve MLOps standards (versioning, model registry, documentation, handover)
• Collaborate with Data Engineers on upstream pipelines and Data Scientists on model design
• Participate in code reviews and coach Data Scientists on software engineering practices
• Monitor cost and performance of the model estate and identify efficiency improvements
• Contribute to scalable patterns and standards for ML at PureGym

Key requirements

• Strong Python and SQL skills with solid software engineering practices (testing, packaging, code review, version control)
• Experience with AI assisted tooling
• Hands-on production experience with ML models
• Experience with MLflow or similar lifecycle tooling for batch/near real-time use cases
• Experience building or working with CI/CD pipelines (GitHub Actions or Azure DevOps)
• Ability to communicate clearly with technical and non-technical colleagues
• Degree in Computing, Engineering, a quantitative discipline or equivalent practical experience
• Advantageous: Databricks experience (Unity Catalog, Jobs/Workflows, PySpark)
• Understanding of infrastructure as code and reproducible deployment practices
• Familiarity with dbt and modern data warehouse/lakehouse patterns

• Independent and ownership-driven
• Pragmatic problem-solving
• Collaborative across Data Science, Data Engineering and Analytics
• Python
• SQL
• software engineering practices (testing, packaging, code review, version control)

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