Senior Machine Learning Engineer (MLOps)

About this role

At ASOS, we're building the next generation of AI-powered Search and Discovery experiences for millions of customers worldwide. Our Search & Recommendations team develops the platforms, infrastructure and machine learning systems that power personalised product discovery, ranking, retrieval and recommendation experiences across ASOS. We operate high-scale production systems that enable data scientists and ML engineers to rapidly develop, deploy and monitor machine learning solutions in a reliable, scalable and cost-effective way.

We're looking for a Senior Machine Learning Engineer - with strong Engineering experience - who enjoys solving complex engineering challenges at scale. This role is ideal for someone with a strong software engineering/MLOps, distributed systems or platform engineering background who wants to work at the intersection of machine learning and production infrastructure. As a Senior Engineer, you'll be responsible for designing, building and operating the platforms and services that enable machine learning models to be trained, deployed and served reliably across ASOS.

You'll work closely with Applied Scientists, Software Engineers, Data Engineers and Product Managers to create the tooling, infrastructure and deployment frameworks that power recommendation systems, search relevance, personalisation and emerging AI applications. This is a highly engineering-focused role with an emphasis on cloud-native systems, platform architecture, automation, observability and operational excellence.

What You'll Be Doing: Design and build scalable machine learning platforms and infrastructure supporting model training, deployment and serving. Develop highly available backend services that power recommendation, search and personalisation experiences for millions of customers. Build and maintain CI/CD pipelines for machine learning and data products. Design batch and real-time inference architectures using modern cloud-native technologies.

Improve reliability, resilience and performance across ML workloads through monitoring, observability and automation. Build tooling and frameworks that enable data scientists and ML engineers to deploy models safely and efficiently. Own production services, infrastructure and operational excellence practices, including incident management and root cause analysis. Optimise distributed compute workloads and resource utilisation across cloud environments.