Global Private Bank, Technical AI Product Manager, AI & Innovation, Vice President at JPMorgan Chase, New York, NY
Global Private Bank, Technical AI Product Manager, AI & Innovation, Vice President at JPMorgan Chase in New York, NY. Est. up to $200k. Apply with swipeandhired.
We are looking for a technical AI product leader to join the AI & Innovation team in the Global Private Bank. This is a hands-on builder and product leader: you will design and build AI prototypes, determine which ideas are worth scaling, and own the product outcome through the Labs-to-Products transition and beyond. Working at the intersection of AI engineering, data science, and product management, you will partner with advisors, product teams, and technology and control partners to turn emerging AI capabilities into solutions that create measurable value for clients and the firm. This role combines hands-on building with end-to-end ownership of product outcomes.
Job Responsibilities
- Design, code, and build AI prototypes hands-on from end to end, spanning data and technical architecture, agentic workflows, orchestration, evaluation, and working demonstrations
- Partner with advisors and business teams to identify high-value problems, translate them into technical solutions, and articulate the business case for investment
- Own the product vision, priorities, and deliverables for AI innovation initiatives, balancing rapid experimentation with a clear path to scalable products
- Define and run evaluations that measure solution accuracy, reliability, risk, user experience, and business value before broader deployment
- Work with Technology, Architecture, Data, Cybersecurity, and Controls to address security, privacy, data quality, auditability, and regulatory requirements throughout development
- Own the product outcome through the Labs-to-Products transition, documenting findings, resolving dependencies, and establishing clear ownership and success measures with the teams responsible for scaling solutions
- Communicate technical results and trade-offs clearly to senior business leaders while translating business needs into actionable requirements for engineering partners
- Push the frontier of applied AI in private banking by rapidly experimenting with foundation models, agentic systems, context engineering, orchestration, and AI-assisted development, then turning the most promising capabilities into working solutions for advisors and clients
Required Qualifications, Capabilities, and Skills
- Significant experience building AI or data-driven products and owning outcomes from discovery and prototyping through delivery
- Strong Python development skills and hands-on fluency with large language models, AI agents, tool use, orchestration patterns, retrieval systems, and context engineering
- Experience building agentic systems that use tools reliably, with guardrails, observability, and human-in-the-loop controls appropriate for a regulated environment
- Experience working with APIs, structured and unstructured data, and modern AI application architectures
- Background in AI engineering, data science, software engineering, or a related technical field, paired with the ability to set product priorities and make business-led trade-offs
- Demonstrated ability to prototype quickly, test assumptions rigorously, and use AI-assisted coding tools effectively
- Experience designing evaluations and using evidence to assess technical performance, risk, user experience, and potential business value
- Strong communication and stakeholder-management skills, including the ability to explain complex technical concepts to senior leaders and translate business needs into actionable requirements for engineers
- Sound judgment when developing solutions in a complex environment, with an understanding of data, technology, operational risk, and control requirements
Preferred Qualifications, Capabilities, and Skills
- Experience in private banking, wealth management, financial services, or another regulated environment
- Experience moving AI prototypes into production in partnership with engineering, product, architecture, and control teams
- Familiarity with advisor workflows and opportunities to improve how advisors serve clients
- Experience with multi-agent systems, agentic coding workflows, or other advanced AI development patterns
- Demonstrated ability to connect emerging AI capabilities to measurable improvements in productivity, client outcomes, or operating efficiency