Associate Director - AI ML and Cloud Security - AI Agent Engineering

Domain focus: AI Agent Engineering (agent layer of the agentic framework); hands-on leadership of an agent engineering team (build, not design).

Job Purpose:

Own the agent layer of the agentic framework — the build, not the design. You run the agent development pipeline: LLM integration, prompt-to-action engineering, agent evaluation and testing, and the reusable agent components the rest of the function depends on. You build GenAI security use cases including alert triage automation and threat summarisation. 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 Cyber Architecture and Solutions 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.

Deep build work in the agent layer, leading a small team while remaining hands-on. The hardest part is testing: agent output is non-deterministic and you will be expected to have a credible answer for how you evaluate it. You build to a specification set by AI Architecture and secure AI security sign-off before release — this is not a role where you set the technical direction.

Proof-of-concept experience will not be enough; we are looking for agents that ran in production.

This role will provide you the opportunity to lead key activities to progress your career. These responsibilities include some of the following:

• Own the agent layer: agent development pipeline, LLM integration, prompt-to-action engineering and agent lifecycle.
• Own agent evaluation and testing, including credible approaches to non-deterministic output.
• Build GenAI security use cases such as alert triage automation and threat summarisation.
• Develop reusable agent components that reduce effort on the next build.
• Build to AI Architecture's specifications and raise design problems rather than engineering around them.
• Apply the binding AI security standards to every agent — tool-invocation control, identity binding, least-privilege scoping — and secure sign-off before release.
• Prepare agent workstream handover packages to the standard the managed service needs.
• Hold AI Platform Engineering to the model-serving endpoints, vector index service levels and token budgets your agents depend on, and elevate when an agent outgrows them.
• Lead the agent engineers building the agent layer, and keep at least a third of your own week on code.
• Run the two-week shadow period where the vendor's operators work your agents under supervision, and hold the go-live gate until their runbook survives a deliberately injected failure.
• Lead your team in using approved AI tools to improve productivity and ways of working, role-modelling responsible use and follow GSK's Responsible AI, privacy and security requirements.
• Mentor the agent engineers and set the engineering standard for the agent layer by example — code review, evaluation discipline and documentation quality.
• Apply GxP and data integrity expectations to agents that touch validated systems or the records they produce.

• 15+ years in cyber security or AI engineering in a…