Engineering Director - AI Solutions (Enabling Functions)

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 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
3. 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
Second-line posture: Ensure governance tooling supports independent review — maintaining separation between the teams that build and the functions that assess, with tool design reflecting this control
Domain-specific requirements: Ensure AI systems handling financially material data (Finance), legally privileged documents (Legal), employee-sensitive information (HR), supplier-confidential data (Procurement), or audit evidence (Audit) meet the specific governance and evidential standards those domains require (e.g., SOX, legal privilege, chain-of-custody, employment law)
Lifecycle governance: Own the ongoing governance obligations for live AI systems — periodic re-assessment, performance review against stated tolerances, change-impact assessment, and documented decision trails for model updates or retirement
4. Operational Resilience — For AI Systems
The second defining pillar. The role requires significant experience in operational resilience as a discipline — not just awareness, but hands-on engineering delivery.
Engineer operational resilience into AI applications — circuit breakers, provider fallbacks, graceful degradation, fail-safe defaults, and dependency-aware architecture so essential functions survive outages
Design for failure: assume model providers, data sources, and integration points will fail, and ensure user-facing services degrade safely rather than catastrophically
Ensure AI systems are mapped against the organisation's important business services framework — with defined impact tolerances, recovery objectives, and tested failover paths
5. Engineering Leadership & Team Development
Lead a multi-disciplinary engineering team comprising software, ML, data, and platform engineers — recruiting, developing, and retaining strong technical talent
Set engineering culture and standards for code quality, testing, documentation, peer review, and production readiness
Develop senior technical contributors and engineering leads — building depth and succession within the team
Mentor and enable non-technical colleagues across enabling functions to s