Vice President, Product Manager - Asset Management Client Service Experience - Service Assist (AI Builder) at JPMorgan Chase, New York, NY
Vice President, Product Manager - Asset Management Client Service Experience - Service Assist (AI Builder) at JPMorgan Chase in New York, NY. Est. up to $200k. Apply with swipeandhired.
As a Product Manager in the Asset Management Product and Experience (AMPX) team, you will help build Service Assist, our generative AI and automation platform that brings client inquiries from every channel into one place and helps our servicing teams respond faster, more accurately, and with less risk.
This role is for a builder. Our strategy is in place, and we need someone to help us deliver it. You will turn ideas into working prototypes quickly, building agents, workflows, AOPs, and proofs of concept that show what is possible and help us get to production sooner. If you already use AI agents to automate parts of your own work and enjoy experimenting with new tools, we would like to hear from you.
Job Responsibilities
- Prototype quickly: build Service Assist features, agents, and workflows using current AI development tools, and turn user problems into working demos that teams can test and give feedback on.
- Create and maintain AOPs and agent workflows for use cases such as email triage, routing, response drafting, summarization, and case management automation.
- Deliver the roadmap: own part of the Service Assist backlog, write clear user stories and acceptance criteria, sequence the work, and take features from discovery through release.
- Partner with Engineering, Design, and Data Science to hand off prototypes, speed up delivery, and move successful experiments into production.
- Spend time with the users: work alongside Client Service teams, learn how inquiries are handled today, and find the next good opportunity to automate or simplify.
- Measure and improve: use SQL, Python, and dashboards to track adoption, response time, accuracy, and automation rates, and use the results to improve what we deliver.
- Test AI responsibly: build evaluation sets, run prompt and model tests, and apply controls so solutions are accurate, explainable, and meet our Risk & Controls standards.
- Drive adoption: create demos, quick-start guides, and training that help servicing teams get value from new capabilities, and share what you know about AI with the rest of the team.
Required qualifications, skills, and capabilities
- Hands-on AI experience: you regularly use AI coding assistants, coding agents, and modern code editors to build prototypes, automations, and tools. Be ready to show us something you have built.
- Working knowledge of AI concepts: prompt engineering, large language models (LLMs), retrieval-augmented generation (RAG), and agent-based AI, and how to apply them to business problems. Much of this role is building the harness that our AI agents work within: the tools, data, instructions, guardrails, and checks that let agents do reliable work.
- Comfortable with code and data: working knowledge of Python, SQL, Git/GitHub, and APIs; able to read, change, and debug code and work with data to test ideas.
- Product delivery experience as a Product Manager, Product Owner, Business Analyst, or similar in an agile environment, including backlogs, user stories, acceptance criteria, and iterative releases. AI is changing how products are built and delivered, and we want someone who is ready to rethink our ways of working rather than just follow the traditional playbook.
- Action-oriented: comfortable with ambiguity, able to move quickly, and happy to show a working prototype instead of a long document.
- Clear communicator: able to explain technical topics in plain language and demo your work to partners at all levels.
- Always learning: you keep up with new AI tools and techniques and bring them back to the team.
Preferred qualifications
- Financial services or Asset Management experience, ideally supporting Client Service, contact center, or client inquiry work.
- Experience building agents or multi-step automations using agent frameworks, Model Context Protocol (MCP) servers, or low-code/no-code platforms.
- Familiarity with AI platforms and tooling, such as LLM platforms, vector databases, AI evaluation and monitoring tools, and MLOps/LLMOps practices.