Help AI-focused startups in Neo4j’s Startup Program succeed with graph + LLM solutions
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
Help startups design and implement LLM-enabled solutions using Neo4j
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
Identify and promote repeatable graph + AI solution patterns across the startup portfolio
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
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
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

Requirements

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
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
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
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
Excellent communication and presentation skills, with the ability to explain complex technical concepts to both technical and non-technical audiences
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

Benefits

medical, dental, and vision benefits
401(k)
paid time off
certain leaves of absence
stock option grant
annual bonus eligibility

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

Neo4jCypherdata modelingclusteringperformance tuningPythonJavacontainerizationAILLMs

Soft Skills

communicationpresentationcustomer successcollaborationproblem-solvingtechnical guidancestakeholder alignmentindependenceadaptabilityrisk identification

Certifications

Bachelor's degree in Computer ScienceBachelor's degree in Information Technology