Full-Time

Vice President Product Management

Agentic AI

Neo4j

Neo4j

1,001-5,000 employees

ACID graph database for connected data

Compensation Overview

$340k - $460k/yr

+ Stock Option Grant + Annual Bonus

Remote in USA

Hybrid

Category
Product (2)
,
Required Skills
LLM
Microsoft Azure
Python
Product Management
RAG
AWS
LangChain
Google Cloud Platform
Requirements
  • 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, Retrieval-Augmented Generation 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 (Amazon Web Services, Google Cloud Platform, Microsoft Azure) and modern data ecosystems, including data warehouses, machine learning platforms, and orchestration frameworks.
  • Education: A Bachelor's or Master's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
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 large language model 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, Field 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.
  • Expected travel: Up to 20%

Neo4j provides a graph database management system that stores data as nodes and relationships to help organizations analyze highly connected data. Its core product, the Neo4j Graph Database, is ACID-compliant and uses Cypher to query and traverse the graph, with additional offerings like AuraDB (cloud hosting), the Graph Data Science library, and Bloom for visualization. Unlike traditional relational databases, Neo4j is built for fast graph traversals and analytics, and it supports a freemium model with paid enterprise options used by many large organizations. The goal is to turn complex connected data into actionable insights through scalable graph storage, advanced analytics, and cloud deployment, positioning the company for broader adoption and potential IPO readiness.

Company Size

1,001-5,000

Company Stage

Grant

Total Funding

$633M

Headquarters

San Mateo, California

Founded

2007

Simplify Jobs

Simplify's Take

What believers are saying

  • GraphRAG demand positions Neo4j as infrastructure for explainable enterprise AI.
  • Google Cloud Marketplace listings reduce procurement friction for Aura and self-managed deployments.
  • A $100 million GenAI investment accelerates product releases and startup ecosystem adoption.

What critics are saying

  • Microsoft Fabric Graph bundles enough graph functionality to weaken standalone purchases.
  • AWS Neptune keeps graph spending inside AWS, pressuring Aura conversion rates.
  • Community Edition sprawl delays monetization and undermines Fleet Manager governance value.

What makes Neo4j unique

  • Neo4j pioneered the property graph model and Cypher query language in 2007.
  • Infinigraph now scales single graphs past 100TB without fragmenting relationships.
  • Fleet Manager unifies AuraDB, self-managed, and on-prem deployments across clouds.

Help us improve and share your feedback! Did you find this helpful?

Your Connections

People at Neo4j who can refer or advise you

Benefits

Health Insurance

Dental Insurance

Vision Insurance

401(k) Retirement Plan

Stock Options

Paid Vacation

Growth & Insights and Company News

Headcount

6 month growth

0%

1 year growth

0%

2 year growth

0%
Neo4j
Jan 27th, 2026
Scaling Without Limits: Infinigraph Is Now Generally Available

Scaling without limits: Infinigraph is now Generally Available. Since Neo4j Inc first introduced the Infinigraph architecture in its Early Access Program, the response from the graph community has been clear: the world's most ambitious AI and data projects need a foundation that can scale data, connections, and context. Today, Neo4j Inc is thrilled to announce that Neo4j Graph Database - Infinigraph Edition is now Generally Available (GA). What began as a breakthrough architectural vision is now a production-ready reality. Infinigraph is designed for organizations that have outgrown the limits of traditional graph scaling. The architecture of "infinite" As Neo4j Inc detailed in its architectural deep dive, Infinigraph represents a fundamental shift in how graph data is stored and processed. By introducing a distributed architecture, Neo4j Inc has eliminated the physical constraints of vertical scalability for querying the ever increasing size of interconnected datasets. This architecture enables its customers to run 100TB+ operational and analytical graph workloads in a single system without fragmenting the graph, duplicating infrastructure, or compromising performance. The GA release solidifies these core innovations: * Horizontal Scaling: Grow the size of the database by adding additional machines/shards to the cluster. * Property Sharding: As explored in its technical breakdown of property sharding, Infinigraph handles massive property sets by distributing them across the cluster. This enables Neo4j Inc to maintain high graph traversal performance while expanding the amount of data managed. Powering high-value workloads from large data sets. Growing data volumes and increasing workload complexity challenge organizations to keep their systems running at scale. Use cases such as financial crime detection and prevention yield significant benefits. To be successful, the platforms running these kinds of workloads need a data architecture that continues to perform even as the volume of data grows and the queries get more complex. Infinigraph is the data layer for high-value workloads at scale. It enables organizations to: * Build Systems for Operational and Analytical Workloads: Support fast queries by storing data and relationships together for fast traversals of complex networks of transactions. * Handle Massive Data Volumes: Distribute data across multiple servers to enable flexible scalability options as data size increases. Fueling the AI revolution: GraphRAG at scale. The timing of this GA release is no coincidence. As Enterprise AI moves from pilot to production, the "Context Gap" has become the primary hurdle for Large Language Models (LLMs). Generic RAG (Retrieval-Augmented Generation) is often not enough. To provide truly accurate, hallucination-free responses, AI agents need the deep, interconnected context that only a Knowledge Graph can provide. However, for global enterprises, that context exists across billions or trillions of data points. Infinigraph is the data layer for GraphRAG at scale. It enables organizations to: * Build Massive Knowledge Graphs: Integrate disparate data sources into a single, unified graph that spans large use cases. * Support Agentic AI: Provide AI agents with "long-term memory" that grows with your business, allowing them to navigate complex relationships across trillions of nodes. * Real-Time Context: Deliver structured context to LLMs in milliseconds, ensuring that your AI applications are as fast as they are intelligent. Enterprise ready. Moving from Early Access to General Availability means Infinigraph is now backed by the full weight of Neo4j support, security standards, and SLAs. It is designed to sit at the heart of your mission-critical stack, providing the reliability you expect from the leader in graph technology with the "infinite" scale required for the future. The limits of the past - memory constraints, storage ceilings, and sharding complexities - are gone. Ready to scale your graph to the next level? Check out the webinar and contact its team to learn more about deploying Infinigraph for your most demanding AI and data workloads.

KMWorld
Dec 10th, 2025
Neo4j Fleet Manager offers unified control plane for graph databases

Neo4j Fleet Manager offers unified control plane for graph databases. Neo4j, a leading graph intelligence platform, is releasing Neo4j Fleet Manager - a unified control plane for managing and monitoring graph databases across any environment including cloud, hybrid, and on-premises. The offering comes as graph adoption accelerates with generative AI and agentic applications, where knowledge graphs have proven essential for accurate and transparent outcomes in production, the company said. Neo4j Fleet Manager gives IT leaders a single operational view of their entire Neo4j footprint: AuraDB cloud services, self-managed Enterprise Edition, and local deployments such as Desktop and Community Edition. It also supports Neo4j Infinigraph, a distributed, sharded architecture launched last month that scales graph workloads across multiple machines and environments at 100TB+ scale. Many enterprises use Neo4j's free Community Edition (CE) for innovation and experimentation, the company said. CE's flexibility and reliability have driven adoption, but in large organizations, it can also lead to sprawl and inconsistent governance. Fleet Manager now gives CIOs and CISOs a single view to monitor and manage CE alongside managed and enterprise deployments, providing the operational footing to scale confidently while maintaining compliance and security standards. Available through Neo4j's Aura Console, Fleet Manager provides: * Centralized administration and policy enforcement: Manage all Neo4j databases across AWS, Azure, GCP, and on-prem infrastructure from one control plane. Tag deployments by team or project, and maintain visibility into license usage and extensions. * Unified observability: End-to-end visibility into database health, performance, and resource utilization. Real-time telemetry simplifies troubleshooting and reduces time to resolution. * Operational automation and consistency: Simplified version upgrades with guided workflows to eliminate manual setups and errors, and standardizes operations to minimize risk and downtime of enterprise data. As enterprises accelerate GenAI, agentic, and data unification initiatives, Fleet Manager provides enterprise governance for distributed graph environments, from experimental Community Edition deployments to global Infinigraph clusters. It enables developers to retain flexibility while giving IT and security leaders consistent oversight and compliance visibility, the company said. "Data is increasingly distributed, but it still needs to be governed as one system of record," said Sudhir Hasbe, president and CPO, Neo4j. "Fleet Manager gives enterprises the visibility and control required to manage graph data wherever it runs, a prerequisite for building reliable, governed foundations for GenAI." Neo4j Fleet Manager is available to all Neo4j customers, including Community Edition users, at no cost. For more information about this news, visit https://neo4j.com.

Neo4j
Dec 8th, 2025
Real-time Analytics News for the Week Ending December 6

* news * real-time analytics news for the week ending december 6. Real-time analytics news for the week ending december 6. In this week's real-time analytics news: The annual Amazon Web Services re:Invent conference yielded numerous announcements from AWS and its partners. Neo4j announced the release of the Neo4j Connector for AWS Glue.

StorageNewsletter
Oct 22nd, 2025
Neo4j Invests $100M in GenAI Expansion

Neo4j Inc. announced a $100 million investment to enhance its role as a key knowledge layer for agentic systems and GenAI infrastructure. This funding supports product innovation, including two new agentic offerings, and a major startup program aiding 1,000 AI-native startups globally over the next year. Neo4j aims to address GenAI deployment challenges by providing the necessary infrastructure for contextual reasoning and accurate, explainable AI outcomes.

Business Wire
Oct 2nd, 2025
Neo4j Invests $100M in GenAI Expansion

Neo4j announced a $100 million investment to enhance its role as a key knowledge layer for agentic systems and generative AI. This funding will support product innovation, including two new agentic AI offerings, and the launch of a major startup program. The program aims to support 1,000 AI-native startups globally over the next year.