AI/ML Product Manager - Payments - Vice President

We are building the next generation of AI-enabled automation for a payments platform—using data, machine learning, and agentic AI to improve reliability, reduce operational risk, and accelerate resolution of payment issues at scale. As a Product Manager / AI Product Manager in the Payments Platform team, you will define and drive the product strategy for AI-powered automation, partnering closely with engineers, operations leads, and control partners. This is a high-visibility role at the intersection of payments domain workflows, ML (especially anomaly detection), and LLM-based systems (RAG, agentic orchestration). Job responsibilities Own the AI automation roadmap for the payments platform, focused on measurable outcomes (e.g., reduced exceptions, faster triage, fewer breaks, improved STP, improved detection/precision). Identify and prioritize high-impact payment workflows suitable for AI augmentation or automation (investigation, reconciliation support, exception classification, root-cause suggestions, alert deduplication, etc.). Lead rapid proof-of-concept (PoC) development using AI/ML to validate value quickly, then scale successful PoCs into production-grade capabilities. Drive anomaly detection strategy (signals, feature sets, model approach, thresholds, monitoring) to detect payment issues, ops anomalies (as applicable), and process breaks early. Translate business and user needs into clear product requirements (PRDs/user stories), acceptance criteria, and phased delivery plans. Partner with engineering/ML teams to design LLM + RAG solutions (knowledge grounding, context retrieval, evaluation, safety/controls, feedback loops). Define and track success metrics (precision/recall for anomalies, false positives, latency, automation rate, operational savings, reliability, control posture). Drive alignment across stakeholders (operations, technology, data, risk/controls) and own end-to-end delivery from discovery to launch and iteration. Leads end-to-end product delivery processes including intake, dependency management, release management, product operationalization, delivery feasibility decision-making, and product performance reporting, while escalating opportunities to improve efficiencies and functional coordination. Leads the completion of change management activities across functional partners and ensures adherence to the firm’s risk, controls, compliance, and regulatory requirements. Effectively manages timelines and dependencies while monitoring blockers, ensuring adequate resourcing, and liaising with stakeholders and functional partners. Required qualifications, capabilities, and skills Experience in product management (or equivalent role) delivering data/AI-enabled products from concept to launch. Deep understanding of machine learning models, with particular strength in anomaly detection techniques and operationalization (monitoring, drift, retraining strategy, alert quality). Hands‑on fluency with Python and SQL (enough to partner effectively, prototype, validate datasets/outputs, and reason about implementation). Strong understanding of LLMs, including RAG, prompt/context design, evaluation approaches, and common failure modes. Familiarity with agentic AI systems (tool‑using agents, orchestration patterns, guardrails, human‑in‑the‑loop designs). Ability to work with structured and semi‑structured data and to define data requirements (quality, lineage, access patterns) for ML/LLM systems. Strong stakeholder management skills in complex environments; able to drive decisions, trade-offs, and execution. 5+ years of experience or equivalent expertise in product delivery or a relevant domain area. Demonstrated ability to execute operational management and change readiness activities. Strong understanding of delivery and a proven track record of implementing continuous improvement processes. Experience in product or platform-wide release management, in addition to deployment processes and strategies. Preferred qualifications, capabilities, and skills Payments industry knowledge (payment flows, exceptions, investigations, reconciliation, messaging/clearing concepts, operational risk). Experience productizing ML in regulated environments (model risk, controls, explainability, auditability, reliability). Experience building/leading evaluation frameworks (offline tests, golden datasets, human review, online monitoring). Prior work delivering automation in high‑scale operational platforms (workflow orchestration, case management, alerting systems). Proficient knowledge of the product development life cycle, design, and data analytics. #J-18808-Ljbffr JPMorgan Chase & Co.