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
We use essential cookies to make this site work. With your permission we would also like to use the optional cookies below. Nothing optional is set until you choose, and you can change your mind at any time from "Cookie settings" at the bottom of every page. Cookie policy.
Essential — keep the site working and secure, and remember the choice you make here. Always on.