About This Role
This engineering role focuses on building, deploying, and operating production-grade machine learning solutions within a modern Databricks environment. The Machine Learning Engineer will work closely with Data Scientists to transition models from development to reliable production, with a hands-on focus on ML pipelines, productionisation, deployment, and ongoing model lifecycle management.
Responsibilities
- Build and automate end-to-end ML pipelines covering feature engineering, model training, scoring, and deployment
- Productionise models developed by Data Scientists, transforming notebooks and prototypes into modular, tested, and production-ready code
- Develop scalable ML solutions using Python, PySpark, Databricks, and MLflow
- Deploy machine learning models into batch and real-time environments through APIs, scheduled workflows, and production pipelines
- Manage model versioning, promotion, and rollback throughout the ML lifecycle
- Implement monitoring and observability across production models, including model and data drift, performance alerts, and logging
- Develop automated retraining processes to maintain model performance and reliability
- Work closely with Data Engineering and Platform teams on CI/CD integration, compute optimisation, and secure deployment patterns
- Maintain strong engineering standards across testing, documentation, code quality, reproducibility, and operational reliability
Requirements
- Strong commercial experience as an ML Engineer, with a clear focus on engineering and productionising machine learning models
- Strong hands-on development skills across Python, PySpark, and SQL
- Commercial experience working with Databricks, MLflow, and Delta Lake
- Proven experience building and operating distributed data and machine learning pipelines
- Experience taking Data Science models from notebooks or development environments into production
- Strong understanding of model deployment patterns, model lifecycle management, and production ML environments
- Experience implementing model monitoring, data/model drift detection, logging, and performance monitoring
- Exposure to CI/CD tooling such as Azure DevOps or GitHub Actions
- Experience with containerisation, APIs, and batch or real-time model deployment
- Ability to collaborate closely with Data Scientists, Data Engineers, and Platform teams whilst remaining firmly focused on ML engineering
Benefits
- Bonus up to 25%
- Referral bonus up to £1,000
Important Details
- Work format: hybrid, 3 days per week in office
About the Company
