ML Engineer

ML EngineerLondon – Hybrid, 3 days per week in officeUp to 85,000VIQU 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 deploymentProductionise models developed by Data Scientists, transforming notebooks and prototypes into modular, tested and production-ready codeDevelop scalable ML solutions using Python, PySpark, Databricks and MLflowDeploy machine learning models into batch and real-time environments through APIs, scheduled workflows and production pipelinesManage model versioning, promotion and rollback throughout the ML lifecycleImplement monitoring and observability across production models, including model and data drift, performance alerts and loggingDevelop automated retraining processes to maintain model performance and reliabilityWork closely with Data Engineering and Platform teams on CI/CD integration, compute optimisation and secure deployment patternsMaintain strong engineering standards across testing, documentation, code quality, reproducibility and operational reliabilityKey Experience Required of the ML Engineer:Strong commercial experience as an ML Engineer, with a clear focus on engineering and productionising machine learning modelsStrong hands-on development skills across Python, PySpark and SQLCommercial experience working with Databricks, MLflow and Delta LakeProven experience building and operating distributed data and machine learning pipelinesExperience taking Data Science models from notebooks or development environments into productionStrong understanding of model deployment patterns, model lifecycle management and production ML environmentsExperience implementing model monitoring, data/model drift detection, logging and performance monitoringExposure to CI/CD tooling such as Azure DevOps or GitHub ActionsExperience with containerisation, APIs and batch or real-time model deploymentAbility to collaborate closely with Data Scientists, Data Engineers and Platform teams whilst remaining firmly focused on ML engineeringApply now to speak with VIQU IT in confidence, or reach out to Katie Dark via the VIQU IT website.Do you know someone great?

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