Senior ML Engineer

About the role

In this role, you will design, build, and operate scalable, production-grade ML systems. You’ll work at the intersection of Machine Learning Engineering, MLOps, and cloud-native infrastructure to enable the successful deployment and operation of AI solutions at scale for a leading UK grocery retailer.

You will collaborate closely with Data Scientists, Engineers, and Architects to transform ML prototypes into reliable, secure, and maintainable production systems. This role combines deep technical expertise with operational ownership, performance optimization, and engineering leadership.

Responsibilities

• Design, build, and maintain scalable, production-grade ML pipelines and infrastructure
• Lead end-to-end deployment of ML models from experimentation through production release and ongoing operation
• Translate Data Science prototypes into robust, maintainable, and production-ready ML services
• Make architectural and tooling decisions balancing scalability, performance, reliability, and maintainability
• Integrate ML solutions into enterprise applications and cloud-native environments
• Establish and maintain CI/CD pipelines for ML systems and infrastructure
• Implement model versioning, experiment tracking, monitoring, alerting, and automated retraining workflows
• Ensure high availability, reliability, observability, and operational stability of ML services in production
• Define and implement standards for monitoring model performance, drift detection, and system health
• Optimize inference pipelines for latency, throughput, scalability, and cost efficiency

Requirements

• Expert-level Python skills and strong experience with modern ML frameworks and production-grade ML applications
• Strong experience with cloud platforms such as AWS and/or Azure
• Hands-on experience with containerization and orchestration technologies, including Docker and Kubernetes (EKS/AKS)
• Experience with Infrastructure as Code tools, such as Terraform
• Deep understanding of MLOps practices, including:
• CI/CD pipelines (e.g., GitHub Actions)
• Model versioning and experiment tracking (e.g., MLflow)
• Workflow orchestration tools such as Airflow
• Automated deployment, monitoring, and retraining workflows
• Strong software engineering fundamentals, including Git, testing, code reviews, documentation, and maintainable coding practices
• Experience implementing monitoring and observability for: Model performance tracking, Data and concept drift detection, System metrics, logging, and alerting
• Solid understanding of data engineering fundamentals, including data pipelines, integration, transformation, and data quality processes (e.g., DBT, Kafka)