Director, Applied AI Product Manager

BNY Mellon•New York, NY

12h•$130,000•Hybrid

About The Position

BNY is seeking a Director, Applied AI Product Manager to join its Innovation Team and lead AI-driven business transformation across the bank. The role combines technical fluency in AI systems with product strategy and execution in large, complex organizations. The successful candidate will build and scale AI-enabled platforms, lead multidisciplinary teams, and drive measurable outcomes in a regulated enterprise environment. The role is based in New York, NY, with four days per week in the office. The Director will define and drive strategy for AI-powered process transformation across enterprise functions, leading cross-functional teams to design, build, and scale agentic and intelligent automation solutions that reduce risk, improve operational efficiency, and enhance client experiences. The position works across Product, Engineering, Data Science, Operations, Risk, and Compliance to ensure solutions are secure, explainable, scalable, and production-ready. The Director will own product vision, strategy, and roadmaps; identify opportunities for AI and automation; oversee orchestration frameworks; and partner with technical teams to operationalize AI at scale. The role also involves executive influence, team leadership, stakeholder alignment, adoption and change management, responsible AI governance, and measurement of business impact. Success metrics include productivity and efficiency gains, cost savings, automation impact, risk mitigation, compliance improvements, and client experience and engagement growth.

Requirements

Bachelor's degree in STEM, Economics, or a related discipline.
Approximately 10 or more years of progressive experience in Applied AI product management, program leadership, or AI-driven platform delivery.
Strong understanding of generative AI and LLM-powered systems.
Strong understanding of agentic architectures and orchestration frameworks.
Strong understanding of distributed systems and microservices.
Strong understanding of event-driven design.
Strong understanding of APIs and integration layers.
Strong understanding of metadata indexing and retrieval systems.
Strong understanding of AI lifecycle management and model performance metrics.
Experience operationalizing enterprise-grade AI systems with secure tenant-aware access controls, observability and telemetry, and production reliability standards.
Demonstrated ability to influence senior leadership and drive alignment across large organizations.

Nice To Haves

Experience in highly regulated industries.
Experience operationalizing AI solutions for Big Tech or Fortune 500 enterprises.
Certifications such as CSPO, A-CSPO, SAFe POPM, CSM, AWS Cloud Practitioner, or a Generative AI specialization.
Advanced degree.

Responsibilities

Define and own product vision, strategy, and roadmaps for AI-driven business process transformation with key business stakeholders.
Develop client segment strategies and contribute to market and competitive analysis, pricing, commercialization, go-to-market, revenue, and organic growth objectives.
Align roadmap priorities with enterprise modernization, AI strategy, and operational efficiency goals.
Identify high-value opportunities for AI, intelligent automation, LLMs, and agentic architectures to eliminate manual workflows and improve decision intelligence.
Translate ambiguous business problems into product tenets, PR/FAQs, and scalable execution plans.
Champion an AI-first mindset, modern product thinking, and experimentation frameworks across the organization.
Oversee orchestration frameworks spanning event-driven architecture, APIs and microservices, workflow engines and state management, metadata-aware retrieval systems, and multi-agent coordination models.
Balance innovation velocity with enterprise reliability and governance.
Partner with Engineering and Data Science to operationalize AI at scale, including model lifecycle management, observability, guardrails, and performance optimization.
Ensure responsible AI governance, including attention to bias, explainability, privacy, data security, and operational resilience.
Influence senior executives on investment prioritization and enterprise adoption.
Translate complex AI and agentic concepts into clear, outcome-driven business narratives.
Drive adoption through executive storytelling and structured change management.
Lead a high-performing product team with strong systems-thinking capabilities.
Remove blockers, align stakeholders, and deliver value consistently across concurrent workstreams.
Partner with senior executives and enterprise stakeholders to align AI strategies with business objectives.
Coordinate across product, design, engineering, and commercial practices to communicate vision, strategy, progress, and measurable impact to leadership.
Drive field-to-product feedback mechanisms to refine AI solutions for enterprise-grade performance.
Enable adoption through playbooks, governance frameworks, and change management strategies.
Define and track success metrics, including portfolio integration and trade-off decisions, portfolio scorecards and enterprise checkpoints, productivity gains, operational efficiency, cost savings, automation impact, risk mitigation, compliance enhancements, and client experience and engagement growth.
Demonstrate measurable business impact through scalable AI platforms and intelligent workflow transformation.