Senior Machine Learning Engineer (MLOps)

In this role you will design, build and operate scalable ML platforms and services that train, deploy and serve models across ASOS. You will collaborate with scientists, engineers and product managers to enable personalised search, recommendations and AI-driven experiences at scale. The role emphasizes cloud-native infrastructure, automation and observability to drive reliability and performance. You will influence architectural decisions and lead engineering practices across the ML platform. This is a hands-on leadership position at the intersection of ML and production systems, offering a meaningful impact on customer experiences at a global scale.

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

• Design and build scalable machine learning platforms and infrastructure for model training, deployment and serving
• Develop highly available backend services powering recommendation, search and personalisation experiences
• Build and maintain CI/CD pipelines for ML and data products
• Design batch and real-time inference architectures using cloud-native technologies
• Improve reliability, resilience and performance across ML workloads through monitoring, observability and automation
• Build tooling and frameworks enabling safe and efficient model deployment by data scientists and ML engineers
• Own production services, infrastructure and incident management including root cause analysis
• Optimise distributed compute workloads and resource utilisation across cloud environments
• Drive 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

• Strong software engineering fundamentals with production-grade systems experience
• Experience building 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 experience with Kubernetes, containers and cloud-native tech
• Experience implementing CI/CD pipelines and automated deployment
• Knowledge of Infrastructure-as-Code tools such as Terraform, Pulumi or CloudFormation
• Experience with monitoring, alerting and observability tooling
• Building reliable, resilient, scalable systems with focus on performance and ops excellence
• Experience with data-intensive systems, streaming technologies or large-scale processing
• Exposure to ML systems, model serving, feature stores, training infra or MLOps
• Experience supporting recommendation systems, search or personalisation is advantageous
• Ability to provide technical leadership and influence architectural direction
• Strong collaboration and cross-functional communication skills
• Experience supporting large-scale model training and inference workloads
• Knowledge of vector search, ranking/retrieval architectures or recommender platforms
• Exposure to LLMs, Generative AI and production AI systems
• Experience building internal developer platforms and enablement tooling

• collaboration
• leadership
• communication
• Python
• Java
• Kotlin