AI​/LLM Product Manager- Senior Associate

Predictive Science Role

Join us in building the next generation of AI and autonomous agents to solve mission-critical operational and banking productivity challenges at the scale of the world's largest bank. In this role, you'll own AI use cases end-to-end—from discovery and problem framing through requirements, delivery, and long-term reliability. You'll design, build, and extend production-grade agentic systems, partnering closely with data science, engineering, and business stakeholders.

Success means more than shipping: you'll be accountable for performance, safety, and day-to-day behavior once real bankers rely on these capabilities.

Key Responsibilities

• 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.

Required Skills & Experience

• 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

Success Profile

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.