AI​/ML Product Manager 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).

• 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.

• 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.

• 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.