Senior Machine Learning Engineer (MLOps)

Description

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.

Drive Infrastructure-as-Code adoption and platform standardisation across machine learning systems.

Contribute to architectural decisions across recommendation, search and AI platforms.

Mentor engineers and promote software engineering best practices across the organisation.

Help shape ASOS's long-term machine learning platform strategy.

Requirements

• We're interested in candidates who bring experience across modern software engineering, distributed systems and machine learning infrastructure. We recognise that expertise can be developed through a variety of backgrounds, including Backend Engineering, Platform Engineering, Site Reliability Engineering (SRE), Cloud Engineering, MLOps or Machine Learning Engineering.
• We'd love to see experience in several of the following:
• Strong software engineering fundamentals with experience designing and building production systems at scale.
• Experience developing distributed systems, microservices or high-throughput backend platforms.
• Strong programming skills in Python, Java, Kotlin, Go, Scala or similar languages.
• Experience building and operating services in AWS, Azure or GCP environments.
• Hands-on experience with Kubernetes, containerisation and cloud-native technologies.
• Experience implementing CI/CD pipelines and automated deployment processes.
• Knowledge of Infrastructure-as-Code tools such as Terraform, Pulumi or CloudFormation.
• Experience with monitoring, alerting and observability tooling.
• Experience building reliable, resilient and scalable systems with a focus on performance and operational excellence.
• Experience working with data-intensive systems, streaming technologies or large-scale distributed processing platforms.
• Exposure to machine learning systems, model serving, feature stores, training infrastructure or MLOps practices.
• Experience supporting recommendation systems, search platforms, personalisation engines or other customer-facing data products is advantageous.
• Comfortable providing technical leadership, mentoring engineers and influencing architectural direction.
• Strong collaboration and communication skills, with experience working in cross-functional product teams.
• Experience supporting large-scale model training and inference workloads.
• Knowledge of vector search, ranking systems, retrieval architectures or recommendation platforms.
• Exposure to LLMs, Generative AI and production AI systems.
• Experience building internal developer platforms, engineering enablement tooling or shared capabilities used across multiple teams.
• Additional Information

Benefits

• Employee discount (hello ASOS discount!)
• Employee sample sales
• 25 days paid annual leave + an extra celebration day for a special moment
• Discretionary bonus scheme
• Private medical care scheme
• Flexible benefits allowance - which you can choose to take as extra cash, or use towards other benefits
• Opportunity for personalised learning and in-the-moment experiences that enable you to thrive and excel in your role
• Company Description
• We’re ASOS, the online retailer for fashion lovers all around the world. We exist to give our customers the confidence to be whoever they want to be, and that goes for our people too. At ASOS, you’re free to be your true self without judgement, and channel your creativity into a platform used by millions.
• But how are we showing up? We’re proud members of Inclusive Companies, are Disability Confident Committed and have signed the Business in the Community Race at Work Charter and we placed 8th in the Inclusive Top 50 Companies Employer list.
• Everyone needs some help showing up as their best self. Let our Talent team know if you need any adjustments throughout the process in whatever way works best for you.