VP, Product Management - Agentic AI

Key Responsibilities

• Strategic Product Ownership: Define and own the AI product strategy, clearly articulating how the Neo4j graph platform uniquely enables key enterprise AI use cases, including Retrieval-Augmented Generation (RAG), knowledge graph construction, AI agents, and LLM grounding.
• Team Leadership: Lead and mentor a team of product managers focused on AI-related areas, providing direction, coaching for maximum impact, and ensuring tight alignment between the product roadmap and core business outcomes.
• End-to-End Product Lifecycle: Drive the product lifecycle from initial discovery through launch and continuous iteration. This involves translating complex customer needs, market signals, and technical constraints into clear, prioritized roadmaps.
• Cross-Functional Collaboration: Partner closely with Engineering, GTM, and Research to bring novel graph + AI capabilities to market, including critical integrations with leading AI frameworks (e.g., LangChain, LlamaIndex) and cloud AI platforms.
• Go-to-Market Strategy: Collaborate with GTM, Sales, and Marketing to shape positioning, packaging, and launch motions that resonate effectively with both technical builders and enterprise buyers.
• Customer & Community Engagement: Engage directly with customers and the developer community to gain a deep understanding of current AI building practices and identify where Neo4j can best remove friction and unlock new value.
• Market Intelligence: Monitor the competitive and ecosystem landscape—including LLM providers, vector databases, AI orchestration frameworks, and adjacent graph players—to identify both opportunities and potential risks.
• External Visionary: Represent Neo4j's AI product vision externally at conferences, in analyst conversations, and with strategic partners and customers.

Required Qualifications

• Proven Product Leadership: 8+ years in product management, including at least 3 years in a senior or leadership capacity, preferably within a developer-focused or data infrastructure company.
• Deep AI Ecosystem Knowledge: Hands-on experience building with or shipping products that incorporate LLMs, RAG pipelines, vector search, AI agents, or other related technologies.
• Strong Technical Acumen: Comfortable engaging with engineers and architects on complex topics such as graph data modeling, embeddings, retrieval architectures, and ML pipelines. Experience with Python or a similar language is a significant plus.
• Customer Focus: A demonstrated history of using qualitative and quantitative customer signals to drive product decisions that result in measurable business outcomes.
• Exceptional Communication & Influence: The ability to align diverse stakeholders around a compelling vision and clearly articulate complex technical concepts to both technical and non-technical audiences.
• Platform Experience: Understanding of developer adoption curves for new technologies and how enterprise data teams evaluate and deploy AI tooling, specifically within developer and data platforms.
• Modern Data Ecosystem Familiarity: Experience with major cloud platforms (AWS, GCP, Azure) and modern data ecosystems, including data warehouses, ML platforms, and orchestration frameworks.
• Education: A Bachelor's or Master's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.