ML Engineer – Production Pipelines & MLOps

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

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.