Role Summary

Help AI-focused startups in Neo4j’s Startup Program succeed with graph + LLM solutions so they adopt Neo4j deeply and grow into long-term, high-value customers.

This is a highly technical, customer-facing role focused on helping AI startups onboard successfully, design effective solutions, and scale their Neo4j use over time. The role blends solution architecture, technical advisory, and ongoing customer engagement to support startups from early experimentation through production adoption.

Key Responsibilities

Technical Leadership for Startup Customers

• Serve as the primary technical point of contact for a portfolio of AI startups in the program.
• Guide teams in designing, building, and deploying Neo4j-based architectures.
• Advise on best practices for Neo4j usage across Aura and self-managed environments, including scaling, performance, and production readiness.
• Troubleshoot implementation challenges and help customers make sound technical tradeoffs.

Graph + AI / LLM Solution Design

• Help startups design and implement LLM-enabled solutions using Neo4j, including RAG, knowledge graphs, and agent-based architectures.
• Translate customer AI requirements into concrete Neo4j-centered architectures, patterns, and implementation guidance.
• Work with common AI frameworks and tooling such as LangChain, LlamaIndex, PydanticAI or ADK, agent frameworks, vector search, and open-source and commercial LLMs.
• Identify and promote repeatable graph + AI solution patterns across the startup portfolio.

Customer Success and Technical Relationship Ownership

• Own the ongoing technical relationship with assigned startups.
• Run regular check-ins, track progress, and proactively identify risks or blockers.
• Help customers move from early experimentation to successful production adoption.

Cross-Functional Collaboration

• Partner closely with the Field AI team to align on architectures, patterns, and technical strategy.
• Feed real-world startup requirements, usage patterns, and product gaps back to Product and Field teams.
• Bridge product gaps where needed through third-party tools, integrations, and pragmatic solution design.

Enablement and Knowledge Sharing

• Contribute to internal enablement content, examples, office hours, and best practices for graph + AI patterns.
• Share lessons learned from startup engagements with broader Field teams.
• Help standardize playbooks, templates, and reference implementations for the Startup Program.

Required Experience and Skills

Neo4j and Graph Technology

• Strong hands-on experience with Neo4j or other graph database, including data modeling, Cypher, clustering/topology, and performance tuning.
• Demonstrated ability to debug and optimize real-world implementations, including queries, data ingestion, and infrastructure decisions.

Solution Engineering and Architecture

• Background in solutions engineering, solution architecture, professional services, customer success, or a similar customer-facing technical role.
• Ability to move fluidly between high-level architecture and detailed implementation guidance.
• Experience working with multiple stakeholders to align on a practical technical approach.
• Awareness of sales processes and expansion motions is a plus.

AI / LLM Expertise

• Experience or strong interest in AI/LLMs and agentic architectures.
• Practical exposure to modern AI tooling, including agent frameworks, vector databases, and open-source LLMs.
• Familiarity with some of the following: LangChain, LlamaIndex, RAG patterns, PydanticAI, ADK, or similar frameworks. Docling, Unstructured.io , PyMuPDF.
• Strong curiosity and aptitude to stay current in the graph + AI landscape.

Customer-Facing Communication

• Excellent communication and presentation skills, with the ability to explain complex technical concepts to both technical and non-technical audiences.
• Comfortable leading customer calls, asking probing questions, and constructively challenging design decisions.
• Proven customer-facing experience in a technical role.

General Technical Background

• Strong technical foundation, ideally from pre-sales engineering, solutions engineering, or professional services.
• Proficiency in programming languages such as Python or Java.
• Familiarity with cloud platforms such as AWS, Azure, or GCP.
• Experience with containerization and modern application architectures.
• Ability to work independently and collaboratively in a fast-paced environment.

Qualifications

• Bachelor’s degree in Computer Science, Information Technology, or a related field.
• Proven experience as a Solutions Engineer, Sales Engineer, Solutions Architect, or in a similar technical customer-facing role.