Senior Machine Learning Engineer (MLOps)

Overview

In this Senior ML Engineer role, you will architect and operate scalable ML platforms that train, deploy and serve models for ASOS’s AI-powered search, discovery and personalization. You’ll partner with scientists, software engineers and product teams to deliver reliable, cloud-native infrastructure and tooling for production ML. You shape platform strategy and drive operational excellence, enabling fast, safe experimentation at scale. This is a hands-on, engineering-led role focused on scalable systems and high-impact ML platforms.

Pay / Benefits

• hello ASOS discount
• employee sample sales
• 25 days paid annual leave + extra celebration day
• discretionary bonus scheme
• private medical care scheme
• flexible benefits allowance

Responsibilities

• Design and build scalable ML platforms and infrastructure for model training, deployment and serving
• Develop high-availability backend services powering search, recommendations and personalization
• Build and maintain CI/CD pipelines for machine learning and data products
• Design batch and real-time inference architectures using cloud-native tech
• Improve reliability and performance via monitoring, observability and automation
• Create tooling to help data scientists and ML engineers deploy models safely and efficiently
• Own production services, infrastructure and incident management practices
• Optimize distributed compute workloads and resource utilization across cloud environments
• Advocate Infrastructure-as-Code adoption and platform standardisation across ML systems
• Contribute to architectural decisions across recommendation, search and AI platforms
• Mentor engineers and promote software engineering best practices
• Help shape ASOS’s long-term ML platform strategy

Key requirements

• Strong software engineering fundamentals with production-scale systems experience
• Experience with distributed systems, microservices or high-throughput backend platforms
• Programming skills in Python, Java, Kotlin, Go, Scala or similar languages
• Experience operating services in AWS, Azure or GCP
• Hands-on Kubernetes and cloud-native technologies
• Experience implementing CI/CD pipelines and automated deployments
• Knowledge of IaC tools like Terraform, Pulumi or CloudFormation
• Experience with observability, monitoring and alerting
• Experience building reliable, scalable systems focused on performance and operational excellence
• Experience with data-intensive, streaming or large-scale distributed processing
• Exposure to ML systems, model serving, feature stores, training infrastructure or MLOps
• Experience supporting recommendation/search/personalisation systems is advantageous
• Comfortable providing technical leadership and mentoring
• Strong collaboration and communication in cross-functional teams
• Experience supporting large-scale model training and inference workloads
• Knowledge of vector search, ranking, retrieval architectures or recommendation platforms
• Exposure to LLMs, Generative AI and production AI systems
• Experience building internal developer platforms and engineering enablement tooling

• collaboration
• communication
• mentoring
• Python
• Java
• Kotlin

…