AI Product Manager (GenAI & Enterprise AI Platforms)

AI Product Manager (GenAI & Enterprise AI Platforms)

Location: Jersey City, NJ (Hybrid – 4 Days Onsite)

Role purpose

Define, prioritize, and deliver AI and GenAI products that solve high-value business problems while satisfying enterprise requirements for risk, security, compliance, usability, adoption, and operational readiness. The role connects business strategy, AIRP platform capabilities, citizen development, and measurable outcomes.

Key Responsibilities

• Lead the strategy, roadmap, and delivery of enterprise AI and Generative AI products supporting high-impact banking use cases, including KYC, Credit Underwriting, Banker 360, Customer 360, Pitch Book Generation, Deal Library Intelligence, Governance Tracking, Financial Crime Quality, and Sanctions Screening.
• Define AI product vision, strategy, AIRP platform roadmap alignment, business cases, product roadmaps, success metrics, and measurable business outcomes across enterprise functions.
• Identify, evaluate, and prioritize AI use cases based on business value, feasibility, data readiness, risk profile, platform readiness, AWS/cloud dependencies, user impact, governance requirements, and strategic alignment.
• Translate business needs into product requirements, user stories, acceptance criteria, operating models, governance requirements, and delivery plans that drive measurable outcomes.
• Own the complete product lifecycle from product discovery, experimentation, MVP definition, pilot planning, production rollout, enterprise adoption, monitoring, measurement, lifecycle management, and continuous improvement.
• Collaborate closely with AI Engineering, AI Research, AIRP Platform, Cloud Engineering, Architecture, Data Engineering, Data Science, UX, Risk, Compliance, Legal, Cybersecurity, Data Owners, Citizen Development, and Business Stakeholders to ensure successful product delivery.
• Coordinate platform dependencies across AWS, Terraform/Infrastructure as Code (IaC), DevOps pipelines, cloud infrastructure, security, governance, and production support.
• Define and monitor product KPIs, including adoption, productivity improvement, cost reduction, cycle-time reduction, accuracy, operational efficiency, risk reduction, user satisfaction, business value realization, and control effectiveness.
• Ensure AI products comply with enterprise governance standards by incorporating Responsible AI principles, human oversight, explainability, auditability, security, data privacy, regulatory compliance, and model governance controls.
• Drive enterprise AI adoption through change management, executive reporting, training, communication, user feedback loops, and adoption strategies, while enabling responsible citizen development using Power Platform, Copilot Studio, Power Apps, Power Automate, and Power BI as part of the organization's AI-first transformation.

Must-have candidate profile

• Experience in product management, digital transformation, AI/ML products, data platforms, enterprise technology, or business transformation.
• Strong understanding of AI, GenAI, LLM capabilities, limitations, risks, and enterprise adoption considerations.
• Ability to translate business problems into AI-enabled product opportunities, roadmaps, requirements, and measurable outcomes.
• Experience working with engineering, data science, research, architecture, risk, compliance, cybersecurity, legal, and business stakeholders.
• Strong roadmap management, prioritization, stakeholder engagement, business-case development, delivery governance, and adoption-management skills.
• Working knowledge of AI platform dependencies, including cloud delivery, data readiness, governance approvals, security reviews, and production support.

Preferred experience

• Banking, financial services, fintech, risk, operations, compliance, customer service, banker productivity, or enterprise productivity background.
• Experience delivering AI copilots, knowledge assistants, automation tools, analytics products, AI platforms, data products, or citizen-development programs.
• Familiarity with AIRP-style platform models, AWS-hosted AI platforms, Power Platform, Copilot Studio, Power Apps, Power Automate, Power BI, AI governance, model risk, data privacy, cybersecurity, and Responsible AI requirements.