16 hours ago
London, United KingdomSenior
Responsibilities
- Design and build scalable machine learning platforms and infrastructure for model training, deployment, and serving.
- Develop highly available backend services supporting recommendation, search, and personalization experiences.
- Build and maintain deployment pipelines and automated deployment processes for machine learning and data products.
- Design batch and real-time inference architectures using cloud-native technologies.
- Improve reliability, resilience, performance, monitoring, observability, and automation across machine learning workloads.
- Build tooling and frameworks that help data scientists and machine learning engineers deploy models safely and efficiently.
- Own production services and infrastructure, including incident management and root cause analysis.
- Optimize distributed compute workloads and resource utilization across cloud environments.
- Drive Infrastructure-as-Code adoption and platform standardization across machine learning systems.
- Contribute to architecture decisions across recommendation, search, and AI platforms.
- Mentor engineers, promote software engineering best practices, and influence long-term machine learning platform strategy.
Requirements
- Strong software engineering fundamentals and 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, containerization, and cloud-native technologies.
- Experience implementing automated deployment processes and deployment pipelines.
- Knowledge of Infrastructure-as-Code tools such as Terraform, Pulumi, or CloudFormation.
- Experience with monitoring, alerting, and observability tooling.
- Experience building reliable, resilient, scalable, and performant systems with a focus on operational excellence.
- Experience 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 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.
- Experience supporting recommendation systems, search platforms, personalization engines, or other customer-facing data products is advantageous.
- Comfort providing technical leadership, mentoring engineers, and influencing architectural direction.
- Strong collaboration and communication skills in cross-functional product teams.
Benefits
- Employee discount and access to employee sample sales.
- 25 days of paid annual leave plus an extra celebration day.
- Discretionary bonus scheme.
- Private medical care scheme.
- Flexible benefits allowance that can be taken as extra cash or used toward other benefits.
- Personalized learning and development opportunities.
Categories
About ASOS
ASOS is a global online fashion and beauty retailer for 20‑somethings, selling own‑brand and third‑party labels via its website and mobile apps. The company earns revenue from direct‑to‑consumer e‑commerce, shipping to customers worldwide and offering marketplace services for brands. Founded in 2000 and headquartered in London, ASOS is a public company listed on the London Stock Exchange and runs in‑house content and fulfillment operations at scale.