AI/LLM Product Manager - Senior Associate

- Define product requirements, features, and roadmap components for AI use cases, translating business problems into agentic solutions and workflows. - Lead end-to-end delivery from pilot through production, owning what the agent does, what it is permitted to do, how it acts under uncertainty, and where autonomy boundaries sit. - Build and extend the agents and the net-new infrastructure they depend on: the services, APIs, and integrations that surround the model and make the system work. - Drive model validation, performance monitoring, drift detection, and iterative improvement, keeping behavior correct as you ship and extend. - Manage backlog prioritization, sprint planning, and delivery tracking. - Partner with stakeholders to identify opportunities for AI-driven efficiency or revenue growth, and run user feedback sessions, folding insights into product enhancements. - Define success metrics and measure business impact, including reliability, adoption, and reuse. - Ensure alignment with data governance, compliance, and risk standards, working across Engineering, Data Science, Controls, and the business.

- 3-6+ years in product management, data products, or technology delivery, or equivalent expertise. - Have shipped an LLM-powered, autonomous, or agentic system to production - Demonstrated experience designing and running evals for such systems as needed to meet product driven success criteria. You reason about probabilistic behavior and drift, not deterministic pass or fail. - Strong understanding of the AI/ML lifecycle (training, validation, deployment, monitoring) - Understanding of production-grade data judgment: what good data looks like, where it degrades, pipeline stability, and how quality propagates into behavior. - Experience building net-new infrastructure and integrations, with the ability to hold your own on architecture, APIs, and data pipelines. - Experience working with cross-functional teams (Tech, Data Science, Business), with strong communication and stakeholder-management skills. - Comfort operating inside a large, regulated institution where governance and controls are design inputs, not obstacles. - Ability to ship AI-enabled products and lead complex programs in large, matrixed organizations. - Previous experience in commercial and investment banking a plus

Ownership mindset. Takes a use case from ambiguous problem to reliable production system and stays accountable for how it behaves. Builds for an institution, not a demo.

Treats governance, auditability, and real constraints as the interesting part of the problem, and knows the difference between a weekend prototype and a system bankers depend on.