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.