Head of AI

Head of AI for ITUK, USA & Spain About the role The Head of AI for IT leads the execution of AstraZeneca’s AI-for-IT strategy, developing, deploying and scaling AI across the IT function. The role embeds AI into core IT practices including the project lifecycle, IT operations, cloud migration, software development, quality, observability and service management. It builds and operates the foundational AI for IT platforms, and leverages enterprise platforms and standards for agent orchestration, knowledge systems and guardrails to deliver secure, reusable and scalable agentic AI.

As AstraZeneca’s recognised internal expert for AI for IT, the role creates a global community that accelerates adoption, sharing and reuse while ensuring measurable value.

Key Responsibilities The role is accountable for turning the AI-for-IT strategy into scaled, secure and measurable transformation across AstraZeneca IT globally AI for IT Strategy and Development Develop and implement the AI-for-IT strategy to maximise speed and value across design, development, delivery, deployment, quality, compliance and operations.

Use a value-stream approach to identify, prioritise and scale high-value use cases across IT.Define adoption roadmaps, investment priorities, operating measures and outcomes.

AI for IT Platforms Influences IT platforms/tooling while leveraging EAI's token management, AI gateway, MCP gateway and agentic orchestration standards.

Leverage agent orchestration standards, knowledge systems, guardrails and Model Context Protocol platforms for agentic AI.Contributes to the enterprise agent catalogue, owned by EAI, keeps agents consistently registered and linked to governance, cyber, legal and privacy.

AI-Native IT Practices Embed AI into the project lifecycle, software development lifecycle, testing, quality, compliance, cloud migration and operations.

Partner on AI-native IT service management, including intelligent ticketing, resolution automation and proactive service management.

Enable AI-assisted development, code review and testing through build-with and build-for principles.

Advance AI-driven observability, anomaly detection, predictive incident management and AI Operations.

Token Economics and Operational Governance Partner with EAI on token economics on shared EAI tooling and standards

Continuously optimise AI for IT value versus costs by adapting LLM models, their usage, architecture and operation.

Ensure agents are secure, observable, resilient, compliant and cost-effective.

Define measurable controls for lifecycle management, performance, risk and value realisation.

Community, Adoption and Expertise Act as a recognised internal expert for AI for IT.Create an IT community of practice that shares knowledge, assets, patterns and reusable agents.

Influence leaders, product teams and practitioners to redesign work and adopt AI safely cess Measures Collaborate with the Enterprise AI organisation to leverage existing AI platforms, standards, patterns and best practices.

Encourage use of and contribute to enterprise standards for agentic AI development.

Increased adoption and reuse of AI agents, platforms and shared patterns across IT.Faster delivery, higher engineering productivity and improved service performance.

Measurable automation of IT workflows and reduction in manual effort.

Improved reliability, observability, compliance and user experience.

Transparent and optimised token consumption and AI operating cost.

Essential Skills, Capabilities & Experience Recognised thought leadership in applied AI, agentic AI or AI-enabled IT transformation

Agentic AI architecture and orchestration

Experience with agentic AI, Model Context Protocol, retrieval and enterprise knowledge systems

Experience with AI-native ITSM,…