Business Enablement – Vice President

Overview

In this role you partner with Markets, Banking and Payments to embed AI into real workflows, delivering scalable, production-ready AI solutions. You will collaborate with Technology and Data & Analytics to create reusable components and libraries that standardize AI adoption across teams. Your work shapes end-to-end AI solutions from architecture to deployment while meeting resilience and risk standards. You help translate domain needs into technical requirements and demonstrate measurable commercial impact.

Pay / Benefits

• equal opportunity employer
• reasonable accommodations for religious practices and disabilities

Responsibilities

• Embed with senior stakeholders to identify high-leverage AI opportunities across Markets, Banking and Payments
• Translate domain context into precise technical requirements and reusable components for at-scale adoption
• Design, prototype, and deploy production AI solutions using internal tooling and systems
• Partner with Technology and CD&A to ensure production solutions meet resilience and risk standards
• Own end-to-end architecture and development for use cases, including data, prompting, orchestration, deployment, and iteration

Key requirements

• Experience in capital markets origination, M&A advisory, treasury services, payments operations, or related Banking/Payments domain
• Strong knowledge of financial services with exposure to Payments and/or Banking workflows
• Hands-on production Python experience in an enterprise environment
• Experience building and deploying large language model applications (prompt engineering, retrieval-augmented generation, agent orchestration)
• Familiarity with modern generative AI components (LLM APIs, vector stores, embeddings, tool/function calling, multi-step agent design, model evaluation)
• Strong analytical skills and detail orientation
• Excellent verbal and written communication with senior stakeholders and engineering partners

• stakeholder management
• clear communication
• analytical thinking
• Python (production-grade)
• large language model applications (prompt engineering, RAG, agent orchestration)
• LLM APIs

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