ML Engineer
Summary
An ML Engineer productionises machine learning models for a leading financial services organisation — building end-to-end ML pipelines, deploying models to batch/real-time environments, and managing monitoring, drift detection and retraining in a Databricks environment. Core stack: Python, PySpark, SQL, Databricks, MLflow, Delta Lake, with Azure DevOps/GitHub Actions CI/CD.
Salary: £85,000 - 85,000 per year
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
- 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
- Azure
- CI/CD
- Databricks
- DevOps
- GitHub
- Machine Learning
- MLflow
- Model Training
- Python
- PySpark
- SQL
- Cloud
More:
We are partnering with a leading financial services organisation undergoing a significant data and technology transformation, building out our Machine Learning capability across the business. This is an ML Engineer role based in London with a hybrid working pattern of 3 days per week in the office, offering a salary of up to £85,000. The role is a hands-on engineering position focused on ML pipelines, productionisation, deployment and ongoing model lifecycle management within a modern Databricks environment.
last updated 39 week of 2026