15 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 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 ML 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.
- Contribute to architecture and long-term strategy for recommendation, search, and AI platforms.
- Mentor engineers and promote software engineering best practices.
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 operating services in AWS, Azure, or GCP environments.
- Hands-on experience with Kubernetes, containerization, and cloud-native technologies.
- Experience implementing automated deployment processes and CI/CD 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 high-performance systems.
- Experience with data-intensive systems, streaming technologies, or large-scale distributed processing.
- Exposure to machine learning systems, model serving, feature stores, training infrastructure, or MLOps.
- Experience with recommendation systems, search platforms, personalization engines, or customer-facing data products is advantageous.
- Technical leadership, mentoring, architectural influence, collaboration, and communication experience.
- 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, production AI systems, internal developer platforms, or engineering enablement tooling.
Benefits
- Employee discount and employee sample sales.
- 25 days of paid annual leave plus an additional 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 in-the-moment development experiences.
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.