AI Platforms and Adoption Lead

Job Purpose

Principal Accountabilities

• Own the end-to-end execution of the bank's AI strategy, translating strategic ambition into an executable roadmap that delivers 250 production-grade AI agents within 18 months.
• Define and manage a prioritised AI agent portfolio, aligned to business value, risk appetite, and regulatory constraints.
• Establish clear success metrics (e.g. productivity uplift, cost take-out, risk reduction, customer experience impact) and ensure benefits realisation.
• Act as the single accountable owner for AI platform delivery, adoption, and scale across the bank.
• Lead the design, build, and evolution of the bank's AI agent platforms, including: LLM orchestration and agent frameworks, Data pipelines and feature stores, Model lifecycle management (MLOps / LLMOps), and Secure integration with core banking and enterprise systems
• Ensure platforms are secure, scalable, resilient, and cloud-ready, capable of supporting hundreds of concurrent AI agents.
• Drive standardisation and reuse, reducing time-to-deploy per agent while maintaining quality and control.
• Partner with Architecture, Cybersecurity, and Infrastructure teams to ensure alignment with enterprise standards.
• Establish and run an "AI Agent Factory" operating model to industrialise agent development and deployment.
• Define repeatable patterns, templates, and toolkits to enable rapid agent creation across business units.
• Oversee the full AI agent lifecycle: Use-case intake and qualification, Design and development
• Testing, validation, and risk sign-off, and Production deployment and monitoring
• Continuously optimise delivery velocity to meet aggressive scale targets without compromising control or quality.
• Drive enterprise-wide adoption of AI agents, ensuring they are embedded into real workflows, not run as pilots.
• Partner with business leaders to: Identify high-impact use cases, Redesign processes around AI agents, Measure and realise tangible outcomes, Lead change management, training, and enablement programmes to build confidence and capability across the bank.
• Act as a trusted advisor to senior stakeholders on how AI can transform productivity and decision-making.
• Embed Responsible AI, model risk management, and regulatory compliance into all AI platforms and agents by design.
• Work closely with Risk, Compliance, Legal, and Model Validation teams to: Define AI controls, policies, and guardrails, Support internal and external audits, and Ensure explainability, traceability, and monitoring of AI agents
• Ensure AI agents meet the bank's standards for data privacy, security, fairness, and operational resilience.
• Evaluate, select, and manage AI technology vendors, hyperscalers, and fintech partners.
• Negotiate commercial models that support rapid scale while controlling cost and vendor lock-in.
• Ensure external solutions integrate cleanly into the bank's AI platforms and governance frameworks.
• Stay ahead of market developments in agentic AI, foundation models, and tooling.
• Define and implement the target operating model for AI platform delivery and adoption.
• Build, lead, and upskill cross-functional teams spanning engineering, data science, product, and change.
• Introduce agile and product-centric ways of working suited to AI delivery at scale.
• Mentor internal teams and help transition from dependency on external vendors to strong internal AI capability.
• Own delivery tracking against the 250-agent roadmap, including milestones, risks, and dependencies.
• Proactively surface delivery, risk, or adoption issues and drive corrective action.
• Demonstrate return on investment and strategic impact of AI platforms and agents.

Qualifications

• 7 + years of experience in applying AI to practical and comprehensive technology solutions including at least 7 years in positions of progressively increasing managerial responsibilities.
• Master's degree in computer science, data science, or a related field.
• 10+ years of banking or financial services experience.
• Led at least 2 large data transformation programs end-to-end preferably in large multi-national banks.
• Experience with Copilot, Copilot Studio, Copilot Foundry, Machine Learning, and Natural Language Processing.
• Experience in program leadership, governance, and change enablement.
• Knowledge of basic algorithms, object-oriented and functional design principles, and best-practice patterns.
• Excellent written and verbal communication skills.
• Analytical and review skills with the ability to draw the correct conclusions from reviews.

Requirements