Senior ML Platform Engineer – Scalable MLOps & Infra

ASOS; Greater London; On-site; GBP 90,000 - 140,000; Full time

Benefits offered by this job

Employee discount

Employee sample sales

Discretionary bonus

Private medical care

Flexible benefits

Learning opportunities

Job summary

ASOS is seeking a Senior Machine Learning Engineer with strong software engineering experience to design, build and operate platforms that enable ML models to be trained, deployed and served reliably at scale across production environments.

You will own scalable ML backends powering recommendations, search and personalization for millions of customers, plus CI/CD pipelines and observability across ML workloads.

Qualifications

• Production systems at scale experience.
• Distributed systems, microservices or high-throughput backends.
• Strong programming skills in Python, Java, Kotlin, Go or Scala.

Responsibilities

• Design and build scalable ML platforms and infrastructure supporting model training, deployment and serving.
• Develop highly available backend services powering recommendations, search and personalization for millions of customers.
• Build and maintain CI/CD pipelines for ML and data products.
• Design batch and real-time inference architectures using modern cloud-native technologies.
• Improve reliability, resilience and performance across ML workloads via monitoring, observability and automation.
• 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 ML systems.

Skills

Software engineering

Distributed systems

Python

Kubernetes

CI/CD

Cloud platforms

MLOps

Leadership

Tools

Docker

Terraform

CloudFormation

Pulumi

Git

Job description

ASOS is seeking a Senior Machine Learning Engineer with strong software engineering experience to design, build and operate platforms that enable ML models to be trained, deployed and served reliably at scale across production environments.

You will own scalable ML backends powering recommendations, search and personalization for millions of customers, plus CI/CD pipelines and observability across ML workloads.