AI Product Manager - Business Line Execution, Senior Vice President

Services provides global solutions that help corporations, financial institutions, public sector and commercial clients optimize operations and drive their business forward. Through our five business lines – Liquidity Management Services, Payments, Trade & Working Capital Solutions, Investor Services and Issuer Services - we provide cash management, payments/receivables solutions, working capital solutions, post-trade securities services and issuer services across Citi’s global network.

Citi is building the next generation of AI-powered financial products across its global Services business. We are seeking experienced SVP-level AI Product Managers to drive the execution of the AI product agenda within a specific business line — Payments, Investor Services, Issuer Services, or Trade.

This role operates at the heart of the business, working at the intersection of commercial product management, client engagement, and AI delivery. You will manage significant components of the Global AI Product roadmap for your business line — contributing to strategic direction, prioritizing use cases, and driving disciplined execution from concept through to commercialization. You will be expected to work with significant autonomy while maintaining close alignment with the Director-level AI leadership above you.

What Makes This Role Distinct

This is a delivery-focused, commercially-accountable, client-engaged role. Unlike the Director-level position which is primarily strategy-setting, this role is defined by:

• Hands-on execution ownership — you are accountable for delivery plans, milestones, and outcomes, not just strategy
• Direct, day-to-day client engagement — you are regularly in the room with enterprise clients validating solutions and co-creating products
• Use case ownership — you manage specific AI use cases end-to-end, from business case through to production and commercial launch
• Commercial discipline — you apply rigorous ROI-thinking to every investment decision and track actuals against projections
• AI Risk Co-ordination Establish and manage a consolidated AI risk framework for TWCS — coordinating across Model Risk Management, Operational Risk, Technology, and the Line of Business to maintain a live, actionable AI risk register.
• AI Testing & Performance Define and own the AI testing strategy across the full model lifecycle — setting performance benchmarks, robustness protocols, and continuous improvement feedback loops tailored to trade finance use cases.
• AI Governance & Implementation Design and implement a fit-for-purpose AI Governance framework — embedding model lifecycle policies, committee structures, and governance controls into Agile delivery workflows, aligned to Citi enterprise standards and emerging regulatory mandates.
• Explainability & Audit Readiness Embed Explainable AI (XAI) solutions across TWCS AI use cases and ensure all models are perpetually audit-ready — maintaining comprehensive model documentation, data lineage artefacts, and regulatory compliance controls.
• Market Intelligence & Program Management  conduct structured market research to identify AI-driven opportunities — translating external signals, client needs, and competitive dynamics into actionable product AI programs.
• AI Use Case Development Define, structure, and prioritize AI use cases across the product and sub product portfolio — ensuring each use case is grounded in client value, commercial viability, technical feasibility, and risk acceptability.
• Post Ops process design and drive how operation process changes after AI is deployed.
• AI Product Ecosystem Build and manage the AI product ecosystem — cultivating partnerships with technology providers, internal platforms, and external innovators to create a coherent, interoperable foundation for AI-powered trade finance products

Key Responsibilities

AI Product Roadmap & Execution

• Own and drive significant workstreams within the Global AI Product roadmap for your business line — translating strategic priorities into detailed delivery plans, managing milestones, and driving cross-functional execution from concept through commercialization
• Prioritize the AI use case backlog based on ROI, strategic fit, and client demand — maintaining a clear, well-groomed product backlog that aligns business objectives with engineering execution
• Balance innovation ambition with business pragmatism and P&L accountabi…