Role overview
We’d all like amazing work to do, and real work-life balance. That’s waiting for you at Sainsbury’s. Think about the scale it takes to feed the nation. The level of data, transactions and variety involved. Then you’ll realise this is a modern software engineering environment, because it has to be. We’ve made significant investment in the standards and principles that shape how we work. We iterate, learn, experiment and champion ways of working such as Agile, Scrum and XP. So you can look forward to exciting opportunities across everything from AI to reusable tech.
In a nutshell
Leads the engineering and deployment of production-ready Machine Learning systems that bring Data Science models to life at scale for Sainsbury's customers. Owns the technical implementation of ML pipelines, infrastructure, and operational excellence, ensuring models perform reliably in production. Shows care for both the customer experience and Data Science colleagues by building robust, maintainable solutions and refusing to walk past technical debt or operational issues.
What you'll work on
- Design and implement production-grade ML pipelines and infrastructure that enable reliable, scalable deployment of machine learning models across Sainsbury's products and services.
- Lead the engineering delivery of complex ML systems, making technical decisions on tooling, frameworks, and implementation approaches that balance performance, maintainability, and development velocity.
- Collaborate closely with Data Scientists and Data Analysts to operationalize models, translating experimental code into production-ready solutions while maintaining model accuracy and establishing robust testing frameworks.
- Establish and maintain MLOps practices including CI/CD pipelines, model versioning, monitoring, alerting, and automated retraining workflows to ensure continuous model performance and reliability.
- Optimise ML system performance through efficient resource utilization, cost management, and performance tuning of inference pipelines, ensuring solutions meet latency and throughput requirements.
- Provide technical leadership and mentorship to ML Engineers, sharing best practices, conducting code reviews, and building team capability in ML engineering principles and production systems.
- Identify and resolve technical debt, performance bottlenecks, and operational issues in ML systems, implementing preventative measures and contributing to architectural improvements.
- Support incident response for production ML systems, diagnosing and resolving model degradation, pipeline failures, and infrastructure issues while implementing improvements to prevent recurrence.
What we're looking for
- Expert-level proficiency in Python and ML frameworks with deep understanding of model deployment and optimization
- Advanced experience with cloud platforms (AWS and Azure) including ML services (Azure ML) and container orchestration (Docker, Kubernetes, EKS/AKS) along with infrastructure as code (Terraform)
- Strong expertise in MLOps tooling and practices including CI/CD pipelines (GitHub Actions), model versioning (MLflow), and experiment tracking
- Proficient in building and optimising ML pipelines using workflow orchestration tools (Airflow)
- Strong understanding of software engineering best practices including version control (Git), testing frameworks, code review, and documentation
- Experience in Monitoring and observability for ML systems including model performance tracking, data drift detection, and system health metrics
- Experience in Data engineering fundamentals including data pipelines, data quality, and integration with data platforms (DBT, Kafka)
- Experience in Security and governance practices for ML systems including model security, data privacy, and compliance requirements
- Extensive experience building and deploying ML systems in production environments at scale
- Demonstrable experience providing technical leadership and mentoring to engineers
- Strong background in debugging complex distributed systems and resolving production incidents