Information Security

Analyst, AI ML and Cloud Security

GSK

London · Greater London · United Kingdom

Overview

As Analyst, AI ML & Cloud Security you will support AI platform engineering and data fabric delivery, configuring environments and maintaining build documentation. You will develop automation to keep CI/CD pipelines healthy and surface issues before they affect delivery, with a strong emphasis on precision and traceability. You work closely with senior engineers to strengthen our AI security and cloud posture, contributing to an expanding agentic framework. This entry-point role offers hands-on growth in AI infrastructure within a security-focused team that governs AI tool safety across Azure and GCP. You will help shape reliable, auditable platform builds that enable a managed service to be

Responsibilities
  • Configure and maintain AI platform environments for development and test
  • Document data fabric build outputs and maintain an accurate build registry
  • Maintain CI/CD pipeline documentation to vendor-handover standards
  • Develop automation scripts for pipeline health monitoring to surface failures early
  • Build and execute test cases for platform and data fabric builds
  • Maintain build records for traceability and auditable platforms
  • Follow architecture specs and raise gaps clearly
  • Support senior engineers across platform and data fabric workstreams
  • Raise defects with actionable detail
  • Build subject matter depth in AI platform engineering and data pipeline delivery
Key requirements
  • Experienced in AI, platform or software engineering with a relevant technical degree — domain focus: AI Engineering (AI platform and data fabric workstreams)
  • Configuring AI platform environments and supporting LLM, agent and RAG pipeline builds
  • Building and running test cases for AI components (prompt-injection, output-validation, regression tests)
  • Python automation for AI pipeline health (data quality, ingestion failures, latency, token usage, cost) with automated alerting
  • Documenting data fabric builds and CI/CD pipelines to vendor-handover standards
  • Working to architecture specifications and raising gaps rather than informal fixes
  • Curiosity
  • Precision
  • Ability to raise gaps early
  • AI platform engineering
  • Data fabric pipelines
  • CI/CD practices

Reference: WJ-747_31161467

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