Senior Machine Learning Engineer 

At Sainsbury’s, Machine Learning is central to how we deliver better experiences and smarter decision-making across the business. As a Senior ML Engineer, you’ll play a key role in one of our cross-functional squads - helping to design, build, and run scalable ML systems that create real value for customers and colleagues.

You will -

Work closely with engineering, data science, product, and architecture peers to deliver robust, production-ready machine learning solutions. Provide technical leadership within the team, contribute to shaping best practices, and drive the successful delivery and sustainability of ML capabilities.

 

What you’ll do:

Lead the technical delivery of ML solutions from design through to deployment, including feature engineering, training, testing, serving, and monitoring. Partner with Data Scientists to co-design scalable model pipelines and infrastructure that enable experimentation, rapid iteration, and reliable production deployments. Work as the technical lead for engineering within a cross-functional squad, collaborating closely with Engineering Managers, Data Science Managers, and Product Managers to ensure successful delivery of ML solutions. Ensure solutions align with architectural principles, engineering standards, and long-term sustainability goals. Contribute to the development our MLOps Platform and software engineering best practices. Mentor engineers, participate in code reviews, and help raise the technical bar. Drive innovation by exploring and implementing tools and patterns that improve scalability, observability, and developer experience. Take ownership of non-functional aspects of ML systems including cost efficiency, scalability, reliability, and maintainability.

 

Who you are:

Experienced ML engineer with a strong record of deploying and operating machine learning systems in production. Deep understanding of the ML lifecycle and associated engineering challenges (feature pipelines, model deployment, observability, drift detection, retraining). Proficient in Python and tools such as MLflow, Airflow, Docker, Github Actions and Azure. Skilled in building scalable, maintainable ML pipelines using modern engineering practices. Experienced with Infrastructure as Code (IaC) and able to define, manage, and version infrastructure using Terraform. Able to work collaboratively in cross-functional teams, balancing technical quality, delivery speed, and business value. Strong communicator and technical contributor who can support and mentor peers. Advocates for automation, engineering excellence, and cost-conscious solution design. Familiar with infrastructure concepts including containerisation, IaC, and cloud platform operations.

Essential Criteria

Azure Machine Learning. Experience working with the Azure Machine Learning API or SDK, to deploy assets such as pipelines, compute targets and models. Infrastructure as code. Experience in using terraform to manage and provision and manage Azure resources, including Machine learning workspaces and Networking components. Github Actions. Designing and maintaining CI/CD workflows for ML Pipelines.

 

 

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