ML Engineer

ML Engineer

London - Hybrid, 3 days per week in office

Up to £85,000

VIQU are partnering with a leading financial services organisation undergoing a significant data and technology transformation, building out its Machine Learning capability across the business. They are seeking an ML Engineer to build, deploy and operate production-grade ML solutions, working closely with Data Scientists to take models from development through to reliable production environments. This is a hands-on engineering role focused on ML pipelines, productionisation, deployment and ongoing model lifecycle management within a modern Databricks environment.

Key Responsibilities of the ML Engineer:

• 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

Key Experience Required of the ML Engineer:

• 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

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