Senior Director, 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:
Lead the function that designs, engineers and governs every AI and machine learning security capability GSK deploys. You own the agentic framework that is modernising security services across the Chief Security Office, the AI and ML platform, and the data fabric that that Cyber Architecture and Solutions designs and builds and a managed service vendor operates. You own the AI security standards that are binding on all GSK AI initiatives — not only those inside that Cyber Architecture and Solutions — and cloud security governance across the Azure and GCP estate. A 17-role function across the UK, India and the US, spanning AI architecture, AI engineering, AI platform and cloud security, business-facing AI advisory, and enterprise AI security authority.
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. This is not an AI governance role with an engineering team attached. You will be setting agent authorisation models and trust boundaries where no industry precedent exists and deciding whether an LLM-based control is reliable enough to depend on for a security outcome in a GxP environment where the consequence reaches medicine supply and clinical data. Your architects and engineers work in LangChain, LangGraph, vector databases and production Kubernetes, and they will expect you to follow the detail and challenge it. If your AI experience is policy frameworks, maturity models and vendor assessments, this role will not suit you and the interview will establish that quickly. Equally, if you have deep AI engineering but have never held a security line against a funded programme under delivery pressure, this will be a hard step.