AI Product Lead

Madison-Davis, LLC; New York (NY); On-site; USD 180,000 - 280,000; Full time

Job summary

Madison-Davis, LLC in New York seeks an AI Product Lead to bridge deep business understanding with hands-on technical execution in Investment Banking. This role goes beyond traditional PM, working with senior AI leadership to prototype AI-powered applications, validate business impact, and drive production adoption.

The ideal candidate has Investment Banking experience and hands-on capability building LLM-powered applications, with the ability to prototype across data integration, backend logic,

Qualifications

• Hands-on experience building LLM-powered applications beyond tutorials or basic prompt engineering.
• Strong communication skills across senior business and technical stakeholders.
• Ability to rapidly prototype across data integration, backend logic, and user-facing applications.

Responsibilities

• Identify high-friction Investment Banking workflows and translate them into practical AI opportunities.
• Design and build AI-powered tools supporting workflows such as pitch preparation, comparable-company analysis, diligence, document preparation, client research, and market surveillance.
• Own the early product lifecycle from opportunity identification and data sourcing through prototyping, validation, deployment, and adoption.
• Integrate LLM applications with financial-data platforms, APIs, internal databases, and enterprise systems.
• Develop context pipelines, evaluation approaches, guardrails, and agentic workflows.
• Partner closely with bankers, analysts, associates, engineers, security, legal, and other stakeholders.
• Evaluate opportunities based on feasibility, implementation cost, business value, risk, and expected ROI.
• Monitor adoption, usage, performance, time savings, and business outcomes for deployed solutions.

Job description

A leading global financial services organization is building a dedicated AI capability within its Investment Banking organization and is seeking an AI Product Lead who can bridge deep business understanding with hands-on technical execution.

This is not a traditional product-management or requirements-gathering role. You will work directly with senior AI leadership and Investment Banking professionals to identify high-value opportunities, rapidly prototype AI-powered applications, validate their business impact, and help drive successful solutions into production and adoption.

The ideal candidate brings experience from Investment Banking or a closely adjacent deal-oriented financial environment and has developed genuine hands‑on capability building LLM-powered applications. You should understand how bankers work while also being technically credible with engineers.

Responsibilities

• Identify high‑friction Investment Banking workflows and translate them into practical AI opportunities.
• Design and build AI‑powered tools supporting workflows such as pitch preparation, comparable‑company analysis, diligence, document preparation, client research, and market surveillance.
• Own the early product lifecycle from opportunity identification and data sourcing through prototyping, validation, deployment, and adoption.
• Integrate LLM applications with financial‑data platforms, APIs, internal databases, and enterprise systems.
• Develop context pipelines, evaluation approaches, guardrails, and agentic workflows.
• Partner closely with bankers, analysts, associates, engineers, security, legal, and other stakeholders.
• Evaluate opportunities based on feasibility, implementation cost, business value, risk, and expected ROI.
• Monitor adoption, usage, performance, time savings, and business outcomes for deployed solutions.

Role Requirements

• Approximately 1–4 years of experience in Investment Banking, corporate finance, M&A advisory, capital markets, equity research, or a closely adjacent financial‑services environment.
• Strong working knowledge of Investment Banking workflows such as pitching, financial modeling, comparable‑company analysis, CIM/OM preparation, diligence, origination, and execution.
• Hands‑on experience building LLM‑powered applications beyond tutorials or basic prompt engineering.
• Ability to rapidly prototype across data integration, backend logic, and user‑facing applications.
• Intermediate proficiency in at least one programming language.
• Understanding of LLM context design, evaluations, guardrails, and cost/quality tradeoffs.
• Ability to determine when agentic workflows are appropriate and when simpler solutions are more effective.
• Strong communication skills with the ability to work across senior business and technical stakeholders.
• Ability to operate independently in ambiguous, fast‑moving environments.

Nice to Have

• Experience deploying generative AI products into production.
• Experience with financial‑data platforms such as PitchBook, FactSet, Bloomberg, or SEC EDGAR.
• Startup, fintech, AI‑product, or entrepreneurial experience.
• Experience building AI applications within regulated or data‑sensitive environments.
• Exposure to crypto, digital assets, trading, brokerage, or asset management.