Director, Enterprise AI Engineer

Introduction:

AstraZeneca is seeking a Director of AI Engineering with exceptional depth in modern AI. We require someone who understands how models work at a fundamental level, not just how to apply them, but why they behave the way they do, where they fail, and how to design something better. Someone whose knowledge of the field is deep enough to distinguish genuine advances from incremental work, and current enough to know where the boundaries are.

This person must also ship. We need a leader who translates that depth into production-grade AI systems at pace, leading a team of specialist engineers to solve, build, and deploy solutions on complex, high-impact problems. Research depth and engineering rigor in the same individual.

This is a hybrid role based in Gothenburg, you will lead a team of AI Engineers and serve as a member of the AI Engineering Leadership Team. Candidates will be assessed on both their technical and leadership abilities, as a portfolio delivery lead, and a practicing engineer who raises engineering standards and leads by example. ​You will collaborate with product, architecture, and platform teams, designers, and leaders to deliver production-grade AI services and assets.

Technical depth is equally critical to this role as leadership and delivery. We require someone who operates at the level of the mathematics, the algorithms, and the code - daily - while leading others to do the same.

Our AI Engineering team is a focused incubator of deep technical talent. We build high-impact, reusable, scalable, production-grade AI assets and incubated products across five thematic areas:

Systems Intelligence - Systematically uncover, encode, and exploit what makes AstraZeneca unique

Systems Optimization - Move AstraZeneca AI from predictive to prescriptive across all core areas of business operations and decision-making

Applied Deep Learning - Deep learning expertise deployed on multi-modal, imaging, and foundational modelling initiatives

Engineering Foundations - Cultivate and strengthen foundational engineering capabilities, cross-deployed and forward-deployed across the portfolio

Product Incubation - Incubate and scale solutions prior to hand-off to enterprise product run teams

Key Responsibilities:

Lead and develop AI Engineers with PhDs and significant industry backgrounds, setting and holding an exceptional technical standard

Contribute hands-on to large, shared production codebases alongside your team

Provide deep technical guidance on AI system design - architecture, training, evaluation, orchestration, and deployment - with demonstrable depth in one or more AI sub-domains

Define and implement golden-path standards for AI product development, software engineering, and applied ML research

Drive operational excellence in production ML systems: reliability, observability, failure handling, monitoring, incident response

Maintain deep, current knowledge of the research landscape; critically evaluate and integrate methods with genuine production applicability

Curate the strategic backlog as a Leadership Team member, focusing on highest-impact problems

Partner with other engineering directors across the broader AI engineering landscape

Translate customer and business demands into clear, actionable software requirements

Essential Skills/Experience:

Significant industry experience delivering production AI systems in large, complex organisations - able to raise the technical bar under real portfolio constraints and enterprise governance

Deep expertise in the internals of modern AI systems; able to reason about model behaviour from first principles, not only empirically

Demonstrable depth in at least one AI sub-domain (e.g., optimization, language, multi-modal, foundational models, agentic systems)

Current grasp of the research landscape; able to critically assess and apply work from leading academic venues

Experience designing and delivering multi-component AI systems and services

BSc/MSc/PhD in Computer Science or related quantitative field

Proven leadership of engineers with PhDs and significant industry backgrounds

Active contributor to large-scale production codebases, shared libraries, or SDKs

Advanced Python; familiarity database solutions (SQL, NoSQL, graph)

Strong software engineering practice: architecture, CI/CD, unit testing, OOP, Git

Algorithm design, optimisation, and scaling from first principles

Cloud platform experience for ML development, API design, and deployment

Able to engage senior business and technical stakeholders with equal credibility

Experience in regulated or compliance-sensitive environments