Lead Product Manager – Graph Platform & AI Integration

Title : Lead Product Manager – Graph Platform & AI Integration Location : Rockville, MD or McLean, VA Target Start Date : ASAP Type : contract Pay Rate : DOE Overview We are seeking an experienced Lead Product Manager to drive the strategy and execution of a next-generation Graph Platform and AI-powered intelligence ecosystem. This role sits at the intersection of regulatory operations, data intelligence, and generative AI, enabling a shift from reactive to proactive decision-making across the enterprise. You will define how graph-based intelligence and AI systems work together to uncover complex relationships, accelerate investigations, and deliver actionable insights at scale. This is a highly strategic role with enterprise-wide impact, shaping how advanced data platforms are leveraged to solve critical business and regulatory challenges. Role Responsibilities Product Strategy & Vision • Own the product vision, strategy, and roadmap for the Graph Platform and AI-Graph integration initiatives • Define how graph technology, search, and AI capabilities combine to power intelligent workflows and decision-making • Drive the evolution from reactive data analysis to proactive insight generation Graph & AI Platform Development • Lead development of graph-based intelligence systems that surface relationships, patterns, and anomalies across complex datasets • Define use cases for graph in: • Information retrieval and search • Entity resolution and relationship mapping • Document processing and intelligence extraction • Oversee integration of graph capabilities with AI-driven platforms and applications Enterprise Platform Ownership • Build and scale a platform offering that can be leveraged across multiple internal teams • Establish best practices for how users interact with graph, search, and AI systems • Ensure the platform serves a diverse set of users, from technical teams to business stakeholders Cross-Functional Leadership • Partner closely with engineering teams (including graph platform teams), AI teams, and data teams to deliver scalable solutions • Collaborate with business leaders and operational users to understand workflows and translate needs into product capabilities • Act as the bridge between technical development and business adoption Stakeholder Engagement & Adoption • Guide internal users on how to effectively leverage graph and AI capabilities • Drive adoption through strong product positioning, communication, and enablement • Influence stakeholders across multiple levels, including senior leadership and enterprise users Key Qualifications • 10+ years of Product Management experience , with a focus on enterprise or platform products • Strong experience with graph technologies , such as Neo4j (experience with Amazon Neptune or similar is a plus) • Proven ability to build products leveraging: • Data platforms and large-scale datasets • Search and information retrieval systems • AI/ML or generative AI capabilities • Deep understanding of: • Graph data modeling and relationship-based analytics • How graph enhances intelligence systems and investigative workflows • Enterprise data ecosystems and platform architecture • Experience working cross-functionally with: • Engineering and data teams • Business stakeholders and operational users • AI or advanced analytics teams • Strong ability to: • Translate complex technical concepts into business value • Drive product adoption across diverse user groups • Operate with autonomy and influence at scale Preferred Qualifications • Experience building AI-enabled or data intelligence platforms • Background in highly regulated or data-intensive environments • Familiarity with enterprise search, knowledge graphs, or document intelligence systems • Experience supporting investigative, analytical, or risk-based workflows Impact of the Role • Enable a shift from reactive workflows to proactive intelligence and insight generation • Empower teams to identify hidden patterns, relationships, and risks through graph-based analytics • Define how AI and data platforms converge to support enterprise-wide decision-making • Drive adoption