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

GSK remains committed to achieving bold commercial ambitions for the future. By 2031, we aim to deliver £40 billion in annual sales, leveraging our existing strong performance momentum to significantly increase our positive impact on the health of billions of patients globally. Our Ahead Together strategy is centred on early intervention to prevent and alter the course of disease, thereby protecting people and supporting healthcare systems.

Our diverse portfolio consists of vaccines, specialty medicines, and general medicines.

Through continuous innovation and a dedicated focus on scientific and technical excellence, we strive to develop and launch new, groundbreaking treatments that address critical health challenges.


Domain focus: AI Platform Engineering and cloud security posture across Azure and GCP.

Job Purpose:

Build and own the AI and ML platform infrastructure for Cyber Architecture and Solutions, and lead engineering delivery of the data fabric pipeline. You translate AI Architecture's data fabric design into built, tested and vendor-ready infrastructure. The design is owned by Architecture, this role builds it, and the vendor operates it once delivered.

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. Infrastructure and data engineering rather than model or agent work. You will own Kubernetes and container orchestration, CI/CD for AI and ML workloads, and the data ingestion and transformation layers beneath the data fabric. The handover standard is the thing that matters: a vendor will operate this pipeline without you, so documentation and test evidence are part of the deliverable. If you want to be building agents, that is a different post in this team.

Key responsibilities:

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

• Own the AKS and GKE clusters, GPU node pools and model-serving tier that every Cyber Architecture and Solutions agent runs on, including capacity, upgrade and resilience planning.

• Lead engineering delivery of the data fabric pipeline, translating architecture specifications into built, tested, vendor-ready infrastructure.

• Own Kubernetes and container orchestration for AI and ML workloads.

• Own CI/CD pipelines for AI and ML delivery.

• Own data ingestion and transformation layers, and govern data ingestion into the fabric.

• Integrate security controls into the platform by design, to the binding AI security standards.

• Prepare data fabric and platform handover packages so the vendor can operate them from day one.

• Own the contract between the platform and agent layers — model-serving endpoints, vector index service levels and the token and cost budget per agent — and renegotiate it when an agent outgrows it.

• Lead the platform engineers delivering the data fabric and model-serving tier, and keep enough of your own week on the estate to make credible design calls.

• Run the shadow and reverse-shadow period where the vendor operates the data fabric under your supervision, and hold the handover gate until their runbook survives an 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.

• Own cloud security posture across Azure and GCP for the AI estate — posture findings, misconfiguration remediation routing and the standards the platform is held to.

• Build and maintain the platform through infrastructure as code, so every environment is reproducible and every change is traceable.

Basic Qualifications:

• 15+ years in platform, cloud or AI security engineering in a regulated environment — domain focus: AI Platform Engineering and cloud security posture across Azure and GCP

• Leading AI/ML platform engineering — Kubernetes and CI/CD for AI/ML workloads, GPU and compute provisioning, model serving, vector database operations, MLOps/LLMOps pipelines, model and prompt versioning and observability

• Building the data fabric pipeline to specification with security by design — encryption at rest and in transit, RBAC, audit logging and classification enforcement — and owning data ingestion governance and change control

• Managing the vendor's agentic transformation of the data fabric and vendor readiness for operations handover, ensuring GSK requirements, classification controls and resilience standards are maintained

• GPU scheduling and model-serving operations at scale — KServe or vLLM behind an autoscaled Kubernetes pool, with p95 latency and cost per thousand tokens held to a published budget.

Preferred Qualifications:

• Terraform, infrastructure as code and cloud-native tooling, with vector databases and AI platform components

• Cloud IAM, workload protection and AI workload detection controls

• Compute and data pipeline infrastructure supporting autonomous AI agent operation

• GPU and compute cost management and capacity planning for AI workloads

• Running a large-scale ingestion or data fabric pipeline in production, including backfill, schema evolution and the cost curve as volume grows.
• Qualifications: Bachelor's degree in a relevant discipline, or equivalent professional experience.
• Typical tools and technologies: Kubernetes with GPU and compute provisioning, model serving using KServe or vLLM, MLOps and LLMOps CI/CD pipelines, model registries and MLflow, vector database operations, data fabric pipeline engineering, Terraform and infrastructure as code, cloud security posture management across Azure and GCP, production Python.

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Why GSK?

Uniting science, technology and talent to get ahead of disease together.

GSK is a global biopharma company with a purpose to unite science, technology and talent to get ahead of disease together. We aim to positively impact the health of 2.5 billion people by the end of the decade, as a successful, growing company where people can thrive. We get ahead of disease by preventing and treating it with innovation in specialty medicines and vaccines. We focus on four therapeutic areas: respiratory, immunology and inflammation; oncology; HIV; and infectious diseases – to impact health at scale.

People and patients around the world count on the medicines and vaccines we make, so we’re committed to creating an environment where our people can thrive and focus on what matters most. Our culture of being ambitious for patients, accountable for impact and doing the right thing is the foundation for how, together, we deliver for patients, shareholders and our people.

Inclusion at GSK:

As an employer committed to Inclusion, we encourage you to reach out if you need any adjustments during the recruitment process.

Please contact our Recruitment Team at IN.recruitment-adjustments@gsk.com to discuss your needs.

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