Role Overview
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.
Context
Enabling functions — HR, Finance, Procurement, Legal, Audit, and Compliance — govern how an organisation hires, contracts, spends, reports, partners, audits, and maintains compliance.
They represent
high-value AI opportunities
and




high-consequence environments
— where outputs carry regulatory, financial, and reputational weight.
Realising value at scale requires engineering leadership that can navigate complex data landscapes, build for reuse, and embed governance, human oversight, and operational resilience into architecture decisions from the outset.
These AI applications do not exist in a vacuum.
Each system must be governed — classified, registered, monitored, auditable, and defensible to regulators, auditors, and internal oversight functions.
The governance and resilience challenge is twofold: building AI systems that are
themselves
resilient and well-governed, and ensuring the frameworks, processes, and controls that surround those systems are robust, proportionate, and continuously maintained.
The role demands someone who has operated at this intersection for a significant portion of their career — not someone encountering governance as a new discipline.
This role is designed for an engineer who has already built AI applications inside a
large, regulated enterprise
, who has
demonstrated experience delivering AI solutions across multiple enabling functions
(e.g., HR, Finance, Procurement, Legal, Audit), who has significant experience governing AI systems and embedding operational resilience disciplines around them, and who treats regulatory requirements as




architecture decisions
— not compliance checkboxes.
Key Responsibilities
1. 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
2. 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 a

📌 Engineering Director (Barcelona)
🏢 AstraZeneca
📍 Barcelona