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 driven systems with increasing scope and complexity.
• Consistent track record of leading large, globally distributed engineering organizations with multiple layers of senior leadership, including org design, succession planning, and coaching.
• Deep experience delivering production AI systems at enterprise scale, including LLM and classical ML solutions, RAG architectures, and agentic or work flow oriented AI applications.
• Hands on familiarity with MLOps/LLMOps practices such as CI/CD for models and prompts, experiment tracking, automated evaluation, monitoring, and incident management.
• Proven success setting and implementing enterprise engineering standards and governance in federated organizations, influencing without direct authority.
• Strong understanding of data and cloud platforms (data warehouses/lakes, streaming, containers, orchestration, GPUs/accelerators, observability) and their role in AI workloads.
• Practical experience embedding Responsible AI and security into engineering practices within regulated or high risk environments, including audit ready processes.
• Executive level communication skills, able to engage credibly with senior scientific, business, and technology leaders and translate sophisticated AI topics into clear decisions and roadmaps.

Desired Skill / Experience

• Background in life sciences or adjacent regulated domains (pharma R&D, clinical development, manufacturing, supply chain, commercial, or healthcare).
• Familiarity with GxP, pharmacovigilance, GDPR, HIPAA, and evolving AI specific regulations.
• Demonstrated thought leadership in AI engineering, such as conference talks, opensource contributions, standards participation, patents, or widely recognized publications.
• Experience building or scaling AI platform, MLOps, or agentic orchestration capabilities that support dozens to hundreds of AI products or use cases.
• Advanced degree in computer science, machine learning, engineering, or a related field, or equivalent leadership experience delivering sophisticated AI solutions.

Why AstraZeneca:

Here you will lead at the edge of applied AI, shaping how sophisticated models and agents become practical systems that improve patient outcomes and business performance. We bring diverse experts together to unlock bold ideas, connect data to decisions, and turn complex information into real-world insights at scale. Your work will be visible—this is a place to raise your personal profile through publishing, speaking, and building recognized engineering patterns—while being supported by a culture that values kindness alongside ambition and celebrates disciplined innovation that makes a difference.

Call to Action:

Lead the engineering shift that makes AI reliably useful at enterprise scale—step into this role and shape the future of AI at AstraZeneca today.

Date Posted

Closing Date

AstraZeneca embraces diversity and equality of opportunity.  We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills.  We believe that the more inclusive we are, the better our work will be.  We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics.  We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.