Engineering Director - AI Solutions (Enabling Functions)

Description

The Enterprise AI function is hiring a senior engineering leader to direct the technical work and production scaling of AI across enabling functions such as HR, Finance, Procurement, Legal, Audit, Compliance and Business Development.

You will build and lead at once: hands-on design and release of multi-agent systems, retrieval pipelines and governed AI applications, while guiding a team from opportunity discovery to live deployment.

You need to turn an idea into a working prototype quickly, and you bring real depth in AI governance, operational resilience and regulatory compliance that shapes how you design, assess and run systems.

You will work with enterprise technology, data engineering, technology governance, legal, information security and business stakeholders to deliver AI that is auditable and compliant from the first commit.

A large part of the work is managing data sovereignty across jurisdictions, handling divergent regulations and governing AI across borders so that systems stay defensible under every applicable regime.

Enabling functions decide how an organisation hires, contracts, spends, reports, partners, audits and stays compliant. Their outputs carry regulatory, financial and reputational weight, so governance, human oversight and resilience go into the architecture from the start.

The company is a large regulated enterprise. The team covers software, ML, data and platform engineering.

Responsibilities

• Set the engineering roadmap for AI across HR, Finance, Procurement, Legal, Audit and Compliance, sequencing delivery and capability work around business priorities.
• Set direction for scalable, governed AI platforms, designing for modularity, reuse across functions and compliance from the outset.
• Make high-impact decisions on architecture, build versus buy, model strategy (foundation models, fine-tuning, multi-provider orchestration, RAG) and integration patterns.
• Build shared components, reusable agent patterns and common governance tooling so the portfolio ships faster.
• Design model routing, data residency and hosting so they respect jurisdictional limits and cross-border transfer rules.
• Write investment cases for senior stakeholders that explain engineering choices in terms of scale, risk, regulatory defensibility and value.
• Design and ship multi-agent LLM systems across several model providers, selecting models against product needs and compliance constraints, including region-specific routing where required.
• Build RAG pipelines over real enterprise data, with single-purpose named agents, hallucination checks before any user sees output, and immutable audit logs at each stage.
• Use AI-assisted development tools to cut delivery time, keeping architecture, model routing and guardrails under human control.
• Lead technical design for complex solutions across enabling functions, including HR policy automation, contract risk scoring, procurement analytics, compliance monitoring, financial forecasting, audit analytics and document intelligence.
• Run rapid experiments with clear engineering gates between proof of concept, pilot and production, judged on real data and success criteria.
• Design and run governance structures for AI systems: classification and tiering, risk assessment, model registration, approvals, monitoring duties and decommissioning criteria.
• Ensure systems meet data protection, AI-specific and sector operational resilience rules, including the EU AI Act risk classification and emerging national frameworks, across divergent regimes.
• Build human-gated consequential actions into agentic systems, with every decision recorded in an audit row showing which model decided what and on what evidence.
• Embed deterministic classification and routing, human oversight, model and data cards, and full audit trails in standard engineering practice.
• Keep governance tooling independent: the teams that build AI must stay separate from the functions that assess it.
• Meet domain standards for financially material data (SOX), legally privileged documents, employee-sensitive information, supplier-confidential data and audit evidence, including chain of custody and employment law.
• Own ongoing governance for live systems, including periodic reassessment, performance reviews against tolerances, change-impact checks and documented decisions on model updates or retirement.
• Build resilience into AI applications with circuit breakers, provider fallbacks, graceful degradation, fail-safe defaults and dependency-aware design.
• Assume model providers, data sources and integrations will fail, and make sure user-facing services degrade safely.
• Map AI systems to the organisation's important business services, with defined impact tolerances, recovery objectives and tested failover.
• Recruit, develop and retain a multi-disciplinary engineering team, building senior contributors and leads to create depth and succession.
• Set standards for code quality, testing, documentation, peer review and production readiness.
• Coach non-technical colleagues in enabling functions to build their own AI applications and grow AI literacy across the functions.
• Plan team structure and capacity across discovery, delivery and sustainment.
• Drive end-to-end delivery from architecture through deployment, scaling, monitoring and lifecycle management.
• Work with enterprise technology to use shared platforms and build on common foundations that give back to enterprise capability.
• Set standards for model serving, data pipelines, API design, integration, security and observability.
• Advance MLOps, including CI/CD, automated testing, performance monitoring, incident management and capacity planning.
• Own the data engineering approach for AI, designing pipelines, feature stores and data products for quality, governance and reuse.
• Set data quality, lineage and governance standards for financial data, privileged documents, employee PII and other personal data under data protection law.
• Monitor model drift and bias across the portfolio, measure variation across runs and recommend targeted refinements.
• Run an AI red-team capability that tests outputs before release and continuously afterwards.
• Build automated fairness tests, explainability pipelines and decision audit trails into standard workflows.
• Manage technical risk across the portfolio, including model degradation, data drift, dependency and integration risk.
• Make sure model assurance reporting meets the needs of internal audit, technology governance and external regulators.
• Align shared infrastructure, platform services, tooling and engineering standards with enterprise technology.
• Spot where a solution from one function, such as contract intelligence in Legal, can be adapted for another, such as supplier risk in Procurement, and scale it.
• Use external partners where they speed delivery or add specialist capability, and shape joint programmes with clear scope, integration design and quality standards.
• Build a scaling model that uses shared services, partners and cross-functional reuse to grow impact without adding headcount in step.
• Act as a trusted technical adviser to functional leaders on AI opportunity, risk, regulatory position and investment.
• Run discovery with each business unit, define real problems and measurable success criteria, and improve adoption through UX iteration and user feedback.
• Explain engineering trade-offs, timelines and constraints so leaders can decide with full information.
• Track adoption and operational impact rather than demos, with KPI and KRI frameworks showing usage, quality and value.
• Represent enabling functions in enterprise AI leadership forums and architecture governance, including architecture review boards, AI policy and standards.
• Share governance and resilience patterns that can speed up delivery in other parts of the business.
• Argue for enabling function needs in the wider enterprise technology agenda so platform priorities reflect regulated, process-critical work.

Requirements

• Have built AI applications inside a large regulated enterprise.
• Have delivered AI solutions across several enabling functions such as HR, Finance, Procurement, Legal or Audit.
• Have designed governance frameworks, built governance tooling and worked in governance roles, not only complied with rules set by others.
• Have substantial experience governing AI systems and building operational resilience around them.
• Treat regulatory requirements as architecture decisions rather than checklist items.
• Combine deep hands-on AI engineering with the ability to lead a team from idea to production.
• Have worked at the intersection of AI, governance and regulation for a significant part of your career.

Conditions

Applications close on 19 Nov 2026. The role was posted on 30 Oct 2026.

The company does not discriminate in employment or recruitment and follows work authorization and employment eligibility verification rules.