Platform Engineer
Posted
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
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
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