Executive Director, Head of AI Engineering

Introduction to role:

Lead AstraZeneca’s Enterprise AI (EAI) engineering agenda and organization to deliver safe, scalable, high impact AI solutions across the company. You will manage the embedded AI engineering function to enhance speed and consistency in building AI solutions. This role involves turning ideas into strong production systems swiftly. You will set standards for large-scale AI system engineering, invent core AI capabilities and platforms, stay ahead of emerging AI techniques, and convene a global AI Engineering community of practice. This role reports to the VP, Head of Enterprise AI Technology and partners closely with peers across AI systems, service coordination, and operational groups, AI Architecture, AI Solution Products, AI Security, and Data Platforms.

Accountabilities

• Define the vision, operating model, and guardrails for AI engineering across centralized and federated teams, aligned with data, cloud, cyber, and regulatory requirements at the organizational level.
• Clarify roles, accountabilities, and engagement models for forward deployed AI engineering squads working with Enterprise AI product and platform teams.
• Build and lead high performing AI engineering teams that partner with business facing AI delivery teams to design pilots, prototypes, and production grade AI solutions.
• Focus the central team on advanced AI engineering (LLMs, RAG, agents, evaluation, optimization) while enabling federated teams through standardized patterns and toolchains.
• Work with the AI Platforms, Services & Operations leader to develop requirements by employing commercial AI platforms and scaling AI/ML Ops. Demonstrate deep engineering insight into how engineers use and expand these services.
• Drive reuse of shared services, pipelines, and components so engineers build on secure, compliant foundations and concentrate on differentiated capabilities for each AI use case.
• Collaborate closely with the AI Architecture leader to define reusable reference architectures and build patterns for LLM, ML, and agentic solutions. Ensure these solutions are practical and implementable by engineering teams.
• Ensure AI agents and AI-enabled services can orchestrate reliably across enterprise applications and digital infrastructure via standard APIs, events, and integration patterns.
• Shape requirements and practices for LLM/ML platforms, feature stores, vector search, inference gateways, prompt and agent orchestration, and evaluation frameworks.
• Drive CI/CD, lifecycle management, monitoring, and automated evaluation for data, models, prompts, and agents to ensure robust, observable, and continuously improving AI services.
• Embed privacy, security, fairness, explainable, and auditable into engineering workflows and runtime systems.
• Partner with AI Security, Cybersecurity, Risk, and Compliance to ensure adherence to AstraZeneca policies and relevant legal and compliance frameworks, leading engineering responses to AI-related issues, drift, and safety events.
• Provide executive level technical leadership on model choices, RAG/grounding strategies, safety tuning, and multi agent orchestration with clear autonomy and human in the loop boundaries.
• Guide focused adoption of emerging AI paradigms so innovations are safe, compliant, and tied to clear value.
• Scale reusable assets—APIs, libraries, prompts, agents, evaluation suites—to reduce time to value and improve quality across AI products and domains.
• Align AI engineering investments with measurable outcomes in scientific velocity, operational productivity, cost efficiency, and patient impact.
• Build a strong global leadership bench and AI engineering organization across ML/LLM engineering, platform/SRE, and applied AI.
• Nurture a culture of transparency, speed, ownership, humility, and collaboration, aligned with AstraZeneca’s values.
• Act as the executive sponsor of the AI Engineering network and unite AI engineers across the Enterprise AI Unit with other key teams. They share standards, patterns, and lessons learned.
• Drive continuous learning so teams maintain an edge on new AI concepts and engineering techniques and convert them into pragmatic practices.
• Shape build versus buy decisions and manage strategic vendors and partners in collaboration with platform and architecture peers, ensuring technical fit and longterm sustainability.
• Represent AstraZeneca in external AI communities, conferences, and industry forums to attract top talent and position the company as a recognized leader in the field of AI systems engineering.
• Leader of leaders, guiding a distributed team within highly matrixed environments.

Essential Skills / Experience

• 15+ years of experience leading engineering teams, including 8-10+ years focused on AI/ML or data drive