AI/ML Engineer

Sainsbury's; Greater London; On-site; GBP 85,000 - 120,000; Full time

Benefits offered by this job

Colleague discount

Pensions and life cover

Bonus up to 10% of salary

Annual leave and additional holiday

Job summary

Sainsbury's is seeking an experienced ML Engineer to build and maintain production-grade ML pipelines and infrastructure. You will enable data science models to deliver value in production, engineering efficient, scalable systems, and collaborating closely with Data Scientists and senior engineers.

You will implement ML systems following established architectures, contribute to MLOps, and help optimize performance and reliability across customer-facing services.

Qualifications

• Strong Python experience with ML workflows and data pipelines.
• Proven experience with cloud platforms (AWS and/or Azure) and containerization (Docker).
• Experience building ML pipelines, CI/CD, and monitoring in production.

Responsibilities

• Builds and maintains production-grade ML pipelines and infrastructure that support reliable deployment of machine learning models across Sainsbury's products and services.
• Implement ML systems following architectural patterns and standards, making decisions within defined frameworks and escalating complex trade-offs.
• Collaborate with Data Scientists and Data Analysts to operationalize models, translating experimental code into production-ready solutions with guidance from senior engineers and testing standards.
• Contribute to MLOps practices including CI/CD pipelines, model versioning, monitoring, and alerting.

Skills

Python

Cloud platforms (AWS/Azure)

Docker

Git

Airflow

Terraform

CI/CD

MLOps

Data pipelines

Kafka

DBT

Tools

Airflow

Terraform

Docker

Git

Kubernetes

DBT

Kafka

Job description

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

Builds and maintains the engineering infrastructure that enables Data Science models to deliver value in production for Sainsbury's customers. Implements ML pipelines, monitoring, and deployment automation that ensure model reliability and performance. Improves things for customers by engineering efficient, well-built solutions and demonstrates respect for quality by building systems that are sustainable and maintainable, collaborating closely with Data Scientists and senior engineers.

What I Am Accountable For

• Build and maintain production-grade ML pipelines and infrastructure that support reliable deployment of machine learning models across Sainsbury's products and services.
• Implement ML systems following established architectural patterns and technical standards, making implementation decisions within defined frameworks and escalating complex trade-offs appropriately.
• Collaborate with Data Scientists and Data Analysts to operationalize models, translating experimental code into production-ready solutions with guidance from senior engineers and adhering to testing standards.
• Contribute to MLOps practices including CI/CD pipelines, model versioning, monitoring, and alerting, ensuring ML systems follow established operational procedures and quality standards.
• Support optimisation of ML system performance through efficient resource utilization and performance tuning of inference pipelines, implementing improvements identified through monitoring and feedback.
• Participate in code reviews and knowledge sharing activities, learning from senior engineers and contributing to team capability through documentation and peer collaboration.
• Identify and elevate technical debt, performance bottlenecks, and operational issues in ML systems, working with senior engineers to implement fixes and preventative measures.
• Respond to incidents affecting production ML systems, supporting diagnosis and resolution of model degradation, pipeline failures, and infrastructure issues under guidance from senior team members.

Essential

What I need to know

• Strong proficiency in Python frameworks
• Proven working experience with cloud platforms (AWS and/or Azure) and containerization (Docker)
• Experience using DevOps tooling to build CI/CD pipelines (GitHub Actions)
• Experience building orchestration pipelines using Airflow
• Experience using infrastructure as code (Terraform)
• Solid understanding of software engineering best practices including version control (Git), testing frameworks, code review, and documentation
• Knowledge of MLOps development lifecylce (including experimentation, deployment of batch and realtime systems)
• Working experience of Data engineering fundamentals including data pipelines and integration with data platforms (DBT, Kafka)
• Experience participating in incident response or system troubleshooting activities

Desirable

• Familiarity with feature stores and model registries for ML metadata management
• Knowledge of ML deployment patterns including A/B testing and canary deployments
• Experience with streaming data architectures and real-time inference
• Understanding of responsible AI practices including model explainability and fairness
• Awareness of ML engineering trends and emerging tools
• Knowledge of cloud cost optimisation for ML workloads

What I Need To Show

Own it

• Take ownership of assigned ML components and features, ensuring they work reliably in production and proactively monitoring for issues within your scope of responsibility
• Deliver on your commitments to the team and stakeholders, completing tasks within agreed timelines and communicating early when you need support or face blockers
• Maintain high standards for your code, writing tests, creating documentation, and responding constructively to code review feedback to ensure quality
• Don't walk past problems you can fix, addressing technical issues within your capability and escalating appropriately when support from senior engineers is needed

Make it better

• Look for ways to improve the ML systems you work on, suggesting optimisations for performance, cost, or maintainability based on your observations and measurements
• Contribute to better developer experience by creating clear documentation, reusable code components, and helpful tools that make it easier for the team to work effectively
• Focus on customer impact in your work, understanding how the ML features you build affect end users and seeking to improve their experience
• Identify inefficiencies in your workflows and propose improvements, learning from senior engineers about better approaches and practices

Be human

• Work collaboratively with Data Scientists, seeking to understand their requirements and asking clarifying questions to ensure you implement solutions that meet their needs
• Learn from senior ML Engineers with openness and curiosity, actively seeking feedback and applying guidance to develop your technical skills and judgment
• Communicate clearly and honestly about your progress, challenges, and learning needs, building trust through transparency and reliability
• Support your teammates by being responsive to questions, sharing what you learn, and contributing positively to team culture through respectful and considerate interactions

We are committed to being a truly inclusive retailer, so you’ll be welcomed whoever you are and wherever you work. Around here, there’s always the chance to try something new - whether that’s as part of an evolving team or somewhere else across the business - and we take development seriously and promise to support you. We also recognise and celebrate colleagues when they go the extra mile and, where possible, offer flexible working. When you join our team, we’ll also offer you an amazing range of benefits. Here are some of them:

Starting off with colleague discount, you'll be able to get 10% off at Sainsbury's, Argos, TU and Habitat after 4 weeks. This increases to 15% off at Sainsbury’s every Friday and Saturday and 15% off at Argos every pay day. We've also got you covered for your future with our pensions scheme and life cover. You'll also be able to share in our success as you may be eligible for a performance-related bonus of up to 10% of salary, depending on how we perform.

Your wellbeing is important to us too. You'll receive an annual holiday allowance, and you can buy additional holiday. We also offer other benefits that will help your money go further such as season ticket loans, cycle to work scheme, health cash plans, pay advance (where you can access some of your pay before pay day) as well access to a great range of discounts from hundreds of other retailers. And if you ever need it there is also an employee assistance programme.

Moments that matter are as important to us as they are to you which is why we give up to 26 weeks’ pay for maternity or adoption leave and up to 4 weeks’ pay for paternity leave.

Please see www.sainsburys.jobs for a range of our benefits (note, length of service and eligibility criteria may apply).