Technical Product Manager – AI & Enterprise Platforms, Vice President

BlackRock•New York, NY

1d•Hybrid

About The Position

Technical Product Manager responsible for leading AI-driven enterprise platform initiatives, with ownership across product strategy, execution, and go-to-market. Drives cross-functional alignment across engineering, infrastructure, and business stakeholders to deliver scalable, enterprise-grade solutions.

Requirements

Experience in Technical Product Management within enterprise or SaaS environments
Proven track record of managing complex, cross-functional initiatives
Strong understanding of AI/ML applications in enterprise platforms
Ability to translate technical concepts into business impact narratives
Experience working with distributed engineering teams and stakeholder groups
Strong analytical and problem-solving capabilities

Nice To Haves

Experience with ServiceNow or enterprise workflow platforms
Familiarity with cloud infrastructure and enterprise system integrations
Hands-on experience with tools such as Python, SQL, APIs, or prototyping tools
Exposure to regulated environments (e.g., FIPS-compliant systems)
Background in data-driven product discovery and experimentation

Responsibilities

Define and execute product strategy aligned to organizational objectives
Translate complex, competing priorities into clear, actionable roadmaps
Drive prioritization decisions across multiple teams and stakeholders
Lead development and adoption of AI-powered solutions (e.g., enterprise chatbots, automation tools)
Build and deploy internal tools leveraging modern AI frameworks and APIs
Identify opportunities to scale automation and reduce manual operational effort
Partner with engineering, infrastructure, operations, and vendors
Drive alignment across large-scale, distributed engineering teams (10+ teams)
Bridge technical and business domains by translating engineering insights into executive narratives
Conduct user discovery and stakeholder interviews to identify friction points
Leverage analytics and AI to synthesize large volumes of signals into actionable insights
Build dashboards and reporting frameworks to support leadership decision-making
Drive adoption of alternative solutions across regulated environments
Design scalable processes, frameworks, and operating models (e.g., workflows, RACI)
Improve engineering productivity and capacity allocation
Ensure quality, reliability, and compliance in product delivery