Platform Engineer – Data Science & AI Platform

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