Platform Engineer – Data Science & AI Platform at ASOS

London, England, United Kingdom -

Full Time

Start Date

Immediate

Expiry Date

01 Oct, 26

Salary

0.0

Posted On

03 Jul, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Apache Spark, Azure Databricks, Azure Data Lake Storage Gen2, Unity Catalog, Terraform, Azure DevOps, GitHub Actions, Kubernetes, Docker, RBAC, CI/CD, Infrastructure as Code, AgentOps, MLOps, Data Engineering

Industry

Retail Apparel and Fashion

Description

Company Description We're ASOS, the online retailer for fashion lovers all around the world. We exist to give our customers the confidence to be whoever they want to be, and that goes for our people too. At ASOS, you're free to be your true self without judgement, and channel your creativity into a platform used by millions. Everyone needs some help showing up as their best self. We're Disability Confident Committed - let our Talent team know if you need any reasonable adjustments throughout the recruitment process Job Description Contribute to building and evolving the platform (infrastructure + reusable abstractions) that standardises data engineering workloads (batch/streaming pipelines, data processing) and traditional ML workflows (feature engineering, training, batch/real-time serving) across teams Implement platform-level IaC, CI/CD, and environment management to support consistent, reproducible workloads across dev/test/prod Build and maintain components using Python and Spark for data processing, shared datasets, and platform services Contribute to shared services for data and ML lifecycle management (data pipelines, experiment tracking, versioning, lineage, permissions), aligned to enterprise governance (e.g. Unity Catalog) Support the implementation and operation of a centralised AgentOps capability (LLM gateway, tool integration, prompt and version management) Contribute to agent-specific lifecycle and safety controls (evaluation pipelines, guardrails, access control), with guidance from senior engineers Enhance observability across both domains: Data & ML Ops: data quality, pipeline reliability, model performance Agent Ops: traces, responses, evaluations, cost and behaviour monitoring Contribute to problem solving across platform reliability, performance, and security for data, ML, and agent workloads Apply security and compliance best practices (RBAC/ACLs, secure configuration, identity and access management), supporting a secure-by-default platform design Collaborate with Data Engineers, Data Scientists, and ML Engineers to enable adoption of platform capabilities across ASOS Tech Contribute to documentation, standards, and best practices across the platform Qualifications Experience in Data Platforms, Data Engineering, Cloud Engineering, or ML Platform Engineering roles, with exposure to Azure Strong hands-on experience with Python and Apache Spark Experience with Azure data platform technologies such as Azure Databricks, ADLS Gen2, and Unity Catalog Working knowledge of security and access management (RBAC, ACLs, identity concepts such as Entra ID) Familiarity with Infrastructure as Code using Terraform Experience with CI/CD (Azure DevOps, GitHub Actions) Basic understanding of Azure networking (vNets, NSGs, Private Endpoints) Exposure to Docker/Kubernetes in cloud environments is beneficial Awareness of AgentOps patterns (LLM gateways, prompt/version control, evaluation, observability) is a plus Good communication and collaboration skills, with a strong focus on learning and continuous improvement Additional Information BeneFITS’ Employee discount (hello ASOS discount!) Employee sample sales 25 days paid annual leave + an extra celebration day for a special moment Private medical care scheme Fixed Annual Payment in addition to your salary each year, it's just an extra thank you from us Opportunity for personalised learning and in-the-moment experiences that enable you to thrive and excel in your role

Responsibilities

Build and evolve a standardized platform for data engineering and ML workflows, including infrastructure and reusable abstractions. Implement IaC, CI/CD, and AgentOps capabilities to support the lifecycle of data, ML, and LLM-based agents.