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Engineering Director - AI Solutions (Enabling Functions)
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ICT
💰 Salary Not shared
📊 Market 7,000 GBP–10,000 GBP/mo (84,000 GBP–120,000 GBP/yr)
About the Company
We're transforming the future of healthcare by unlocking the power of what science can do for people, society and the planet. For more information, visit www.astrazeneca.com. Community Guidelines: bit.ly/2MgAcio Pharmaceutical Manufacturing Specialties: Pharmaceutical, Biopharmaceutical, Innovation, Research and development, Global business
About the Role
A senior engineering leadership role responsible for the technical direction, hands-on delivery, and production scaling of AI solutions across enterprise enabling functions - including HR, Finance, Procurement, Legal, Audit, Compliance, and Business Development . This is a builder-leader role . The Engineering Director combines deep hands-on AI engineering - designing and shipping multi-agent systems, RAG pipelines, and governed AI applications - with the technical leadership required to drive a team from opportunity identification through to production deployment. They bring a rare and deliberate combination: the ability to move from idea to working proof-of-concept in days, alongside significant depth in AI governance , operational resilience , and regulatory compliance - not as adjacent knowledge, but as a core professional discipline that shapes how they build, assess, and operate AI systems. The role sits within the Enterprise AI function and works in close partnership with enterprise technology, data engineering, technology governance, legal, information security, and functional stakeholders to deliver AI that is production-grade, auditable, and compliant from the first commit - not retrofitted at the end. Given geographic considerations, the role carries particular responsibility for navigating multi-jurisdictional data sovereignty, regulatory divergence, and cross-border AI governance - ensuring systems are defensible under all applicable regulatory regimes. Conte
Responsibilities
- ● Technical Direction & Architecture
- ● Lead the engineering roadmap for AI across enabling functions, aligning architecture, delivery sequencing, and capability development to business priorities across HR, Finance, Procurement, Legal, Audit, and Compliance
- ● Set architectural direction for scalable, governed AI platforms - designing for modularity , cross-functional reuse , and compliance from the outset
- ● Make high-consequence technical decisions on architecture, build-vs-buy, model strategy (foundation models, fine-tuning, multi-provider orchestration, RAG), and integration patterns
- ● Drive platform thinking over project thinking - building shared components, reusable agent patterns, and common governance instrumentation that accelerate delivery across the portfolio
- ● Ensure architecture accounts for data sovereignty requirements - model routing, data residency, and hosting decisions that respect jurisdictional boundaries and cross-border transfer requirements
- ● Shape investment cases for senior stakeholders, articulating engineering decisions in terms of scalability, risk, regulatory defensibility, and value creation
- ● Hands-On AI Engineering & Delivery
- ● Design and ship multi-agent LLM architectures across multiple model providers, choosing models against product requirements and compliance constraints - including sovereignty-aware routing through region-specific infrastructure where required
- ● Build RAG pipelines over real enterprise corpora with named single-purpose agents, hallucination guards before any user-facing output, and immutable audit logging at every stage
- ● Use AI-assisted development tooling to compress delivery from months to days, while keeping architecture decisions, model routing, and guardrails under deliberate human control
- ● Lead technical design for complex solutions spanning enabling functions - HR policy automation, contract risk scoring, procurement analytics, compliance monitoring, financial forecasting, audit analytics, and document intelligence - with governance built in from the first build
- ● Ensure rapid experimentation capability with clear engineering gates between proof-of-concept, pilot, and production - measuring against real data and real success criteria, not mock demos
- ● AI Governance & Regulatory Compliance
- ● This is a defining pillar of the role. The organisation requires an engineering leader with significant, demonstrated experience in AI governance - someone who has designed governance frameworks, built governance tooling, and operated in governance roles - not simply complied with governance requirements set by others.
- ● Governance architecture : Design and operate the governance structures that surround AI applications - classification and tiering, risk assessment, model registration, approval workflows, ongoing monitoring obligations, and decommissioning criteria
- ● Regulatory compliance (multi-jurisdictional) : Ensure systems meet requirements under applicable data protection laws, AI-specific regulations (e.g., EU AI Act risk classification, emerging national AI frameworks), and sector-specific operational resilience expectations - navigating divergence and maintaining defensibility under multiple regimes
- ● Data sovereignty : Design data-residency and model-routing approaches that respect adequacy arrangements, data-transfer mechanisms, and sovereignty constraints - ensuring processing is appropriately separated by jurisdiction where required, with sovereign model options (e.g., region-specific cloud deployments, local model hosting)
- ● Responsible agentic architecture : Design systems where AI agents reason autonomously but consequential action is human-gated - with every decision writing an audit row recording what model decided what, on what evidence
- ● Governance-by-design : Embed deterministic classification/routing layers, human-in-the-loop oversight, model/data cards, and full audit trails into standard engineering practice - treating these as first-class architecture components, not afterthoughts
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