AI Product Manager

Required Skills & Experience

Required Qualifications
• 3 or more years of relevant experience in product management, including AI and ML, data, or digital transformation products, including ownership of complex products from discovery through production.
• Demonstrated experience defining product strategy, roadmaps, outcomes, requirements, backlogs, release plans, and adoption measures for enterprise technology or data products.
• Strong understanding of generative AI and agentic AI concepts, including LLMs, retrieval-augmented generation, embeddings, agents and tools, workflow and state, model evaluation, human-in-the-loop controls, and AI guardrails.
• Experience translating complex business journeys and stakeholder needs into prioritized, testable product requirements and incremental releases.
• Proven ability to partner effectively with solution architects and engineering leaders while maintaining clear product ownership for business outcomes, scope, priority, and delivery.
• Experience managing cross-functional delivery involving data science, data engineering, software engineering, UX and design, architecture, platform, security, risk, legal, compliance, operations, and business teams.
• Ability to define and interpret product metrics spanning business value, user behavior, quality, reliability, safety, cost, operational performance, and adoption.
• Experience applying agile product practices, including discovery, hypothesis definition, user stories, acceptance criteria, backlog management, release planning, demos, and retrospectives.
• Understanding enterprise AI risks and controls, including privacy, security, responsible AI, model risk, explainability, auditability, resilience, and human oversight.
• Strong stakeholder leadership, facilitation, negotiation, and communication skills, with the ability to align senior business and technology leaders around decisions and trade-offs.
• Ability to work through ambiguity, make evidence-based prioritization decisions, and balance value, user experience, feasibility, risk, cost, reuse, and time-to-market.
• Bachelor's or advanced degree in business, computer science, engineering, data science, information systems, or a related discipline, or equivalent relevant experience.
Preferred Qualifications
• Experience in financial services. Commercial Banking knowledge is strongly preferred, including lending, underwriting, credit, client onboarding, relationship management, treasury services, and payments.
• Experience launching enterprise generative AI, conversational AI, intelligent automation, knowledge retrieval, decision-support, or agentic workflow products.
• Familiarity with AWS or Azure AI services, enterprise model platforms, model gateways, vector search, evaluation tools, observability platforms, and agent development frameworks.
• Experience with product analytics, experimentation, user research, service design, journey mapping, process redesign, and value realization.
• Working knowledge of APIs, event-driven systems, data products, workflow platforms, systems of record, cloud-native delivery, and MLOps and LLMOps concepts.
• Experience preparing products for architecture, privacy, security, responsible AI, model-risk, legal, compliance, and operational-readiness reviews.
• Relevant product management or agile certification is helpful, but demonstrated product ownership and delivery outcomes are more important.
Core Product Leadership Profile
Capability Representative Scope
Product Strategy Vision, customer problems, outcomes, roadmap, investment priorities, value realization
AI Product Design Generative AI, Agentic journeys, RAG, human oversight, evaluation, guardrails
Delivery Ownership Discovery, MVP, pilot, backlog, releases, dependencies, launch readiness
Business and User Adoption Journey design, user validation, change management, training, adoption metrics
Measurement and Operations Business KPIs, quality, safety, latency, cost, reliability, production feedback
Risk and Governance Privacy, security, responsible AI, model risk, auditability, operational controls

Job Description

We are seeking an experienced AI Product Manager to own the strategy, prioritization, delivery, adoption, and measurable business impact of generative AI and agentic AI products for transformational Commercial Banking journeys. Working closely with the AI Solution Architect, business leaders, design, data science, engineering, risk, and operations teams, this role will ensure that AI capabilities solve the right customer and employee problems and are delivered as trusted, usable, production-ready products.
The successful candidate will translate business needs into a clear product vision, outcome-based roadmap, prioritized backlog, and testable product requirements. The role owns delivery from discovery through launch and ongoing improvement, aligning business value, user experience, architecture, data readiness, model behavior, controls, operational change, and adoption across the end-to-end journey.
Role impact: Own AI products that match validated business needs, deliver measurable outcomes, earn user trust, and operate safely within enterprise risk, technology, and responsible AI expectations.
What You Will Do
• Own the product vision, strategy, roadmap, investment priorities, and success measures for one or more generative AI or agentic AI products supporting Commercial Banking journeys.
• Lead business and user discovery to define target users, pain points, jobs to be done, journey gaps, decisions, controls, exceptions, and the outcomes the product must improve.
• Partner with the AI Solution Architect to translate product intent into an end-to-end solution approach, aligning business requirements with agent responsibilities, orchestration, integrations, data and knowledge needs, human oversight, and non-functional requirements.
• Define product requirements, user stories, acceptance criteria, workflow behavior, human-in-the-loop decision points, exception handling, and operational readiness needs for AI-enabled journeys.
• Prioritize the backlog using business value, user impact, feasibility, risk, data readiness, dependencies, cost, latency, scalability, and time-to-market.
• Own delivery across discovery, prototyping, proof of concept, minimum viable product, pilot, production launch, adoption, measurement, and continuous improvement.
• Coordinate product execution across business, design, architecture, data science, data engineering, software engineering, platform, security, legal, compliance, model risk, operations, and change management teams.
• Define evaluation and release criteria for AI features, including task success, groundedness, quality, safety, latency, cost, reliability, user experience, human escalation, and business outcome measures.
• Ensure product requirements address responsible AI, privacy, security, model risk, explainability, auditability, entitlements, records management, resiliency, observability, and appropriate human control.
• Manage product risks, assumptions, dependencies, decisions, and trade-offs, and escalate impediments and drive timely resolution across participating teams.
• Lead pilot design and user validation, incorporate qualitative and quantitative feedback, and make evidence-based decisions about launch readiness, scope, and iteration.
• Define launch, operating, support, incident, adoption, training, and change management plans with business and technology owners.
• Monitor product performance after launch, including business outcomes, user adoption, model and agent quality, operational exceptions, cost, risk indicators, and production feedback.
• Communicate product direction, decisions, progress, risks, and outcomes through roadmaps, journey maps, product briefs, demonstrations, executive updates, and governance forums.
• Identify reusable product capabilities and patterns that can accelerate additional agentic AI journeys while preserving clear ownership and measurable value.