Analyst, AI ML and Cloud Security

Analyst, AI ML & Cloud Security

Why Us ! At GSK, we unite science, technology and talent to get ahead of disease together. We are a global biopharma company with a special purpose - to unite science, technology and talent to get ahead of disease together. We're proud to be leading a healthcare revolution, and we're looking for people who can help us deliver on our ambition

Job Purpose:

Support AI engineering delivery on the platform and data fabric workstreams. You configure AI platform environments, document data fabric build outputs, maintain CI/CD pipeline documentation, and develop the automation that keeps the builds moving and surfaces failures before they affect delivery.

About the team:

AI ML and Cloud Security is the function through which GSK is transforming security itself. The team designs and builds the agentic framework, a multi-agent platform moving security services across the Chief Security Office from rule-based to AI-driven operation. It owns AI architecture and AI engineering end to end: the agent layer, LLM orchestration, the AI and ML platform, and the data fabric pipeline that CAS designs, builds and tests before a managed service vendor operates it. The team also governs AI tool security posture, advises on cloud security across the Azure and GCP estate, and is the front door between GSK's business units and R&D and the technical AI function. AI security standards set here are binding on every AI initiative in GSK. Seventeen roles across the UK, India and the US.

What makes this role different:

An entry point into AI platform engineering for someone with a scripting and automation background who wants to build depth in AI infrastructure. You will support senior engineers rather than lead workstreams, and a real share of the job is documentation and test evidence that has to be right, because a managed service vendor will operate the data fabric from it without anyone from CAS present. Curiosity and precision matter more here than existing AI experience.

Key Responsibilities:

• Configure and maintain AI platform environments for development and test.
• Document data fabric build outputs and keep the build registry accurate.
• Maintain CI/CD pipeline documentation to the standard the vendor needs to operate the fabric once it transfers.
• Develop automation scripts for pipeline health monitoring so failures surface before they affect delivery.
• Build and execute test cases for platform and data fabric builds.
• Maintain build records so the platform estate stays traceable and auditable.
• Work to the architecture specifications provided and raise gaps rather than filling them informally.
• Support senior engineers across the platform and data fabric workstreams.
• Raise defects and blockers early with enough detail to be actionable.
• Build subject matter depth in AI platform engineering and data pipeline delivery.

Why You!

We are seeking professionals with the following required skills and qualifications to help us achieve our goals:

Basic Qualifications:

• 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 - environment setup, embedding and vector index jobs, and evaluation runs
• Building and running test cases for AI components - prompt-injection, output-validation and regression tests for non-deterministic behaviour
• Python automation for AI pipeline health - data quality, ingestion failures, model and API latency, token usage and cost - with automated alerting
• Documenting data fabric builds and CI/CD pipelines to vendor-handover standard and maintaining build records, working to architecture specifications and raising gaps rather than filling them informally.

Preferred Qualifications:

• You have debugged a failing vector index rebuild and can explain what you changed.
• Azure or GCP fundamentals certification (AZ-900, AI-900 or equivalent)
• Working knowledge of prompt-injection test patterns from the OWASP Top 10 for LLM Applications, enough to extend an existing test suite.
• Terraform and Helm for environment provisioning, with enough Git discipline to work in a shared repository.
• Exposure to MLOps tooling - MLflow, model registries or CI/CD for model deployment.

Closing Date:

6th November 2026

Working Pattern:

This role is based in the United Kingdom and offers a hybrid working model, with 2/3 days a week onsite at our central London HQ.