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