This is a hands-on architect role - 100% hands-on. You will write code, design schemas, and ship production systems alongside the team.

- Design and implement RCG schemas, ontologies, and reference patterns for client engagements

- Write production-grade Python, Cypher/Gremlin, and orchestration code (LangGraph or equivalent)

- Use AI coding tools (Claude Code, Cursor, or equivalent) as a force multiplier to ship faster without sacrificing quality

- Build and operate knowledge graphs on Neo4j and Amazon Neptune at enterprise scale

- Design federated KG architectures (Schema KG vs. Instance KG separation) for multi-tenant deployments

- Lead technical conversations with client architects, R&D directors, and CIOs

Must-Haves

- 8-12 years of software engineering experience, with at least 3-4 years in AI/ML systems

- Production knowledge graph experience : designed schemas, written non-trivial Cypher/Gremlin/SPARQL, operated Neo4j, Amazon Neptune, or TigerGraph at meaningful scale

- Strong Python and modern AI tooling : LangGraph, LangChain, or equivalent agent orchestration frameworks

- Hands-on experience with KG-augmented AI : GraphRAG, ontology-driven reasoning, or KG-augmented LLM retrieval

- Daily, fluent use of AI coding tools - Claude Code, Cursor, Kiro, GitHub Copilot, or equivalent.

- You should be able to demonstrate how you use these tools to ship production code, including prompting strategy, multi-file refactoring, agentic workflows, and where you trust them vs. override them

- Enterprise software shipping experience : data governance, lineage, multi-tenancy, observability

-This is a hands-on architect role - 100% hands-on. You will write code, design schemas, and ship production systems alongside the team.

- Design and implement RCG schemas, ontologies, and reference patterns for client engagements

- Write production-grade Python, Cypher/Gremlin, and orchestration code (LangGraph or equivalent)

- Use AI coding tools (Claude Code, Cursor, or equivalent) as a force multiplier to ship faster without sacrificing quality

- Build and operate knowledge graphs on Neo4j and Amazon Neptune at enterprise scale

- Design federated KG architectures (Schema KG vs. Instance KG separation) for multi-tenant deployments

- Lead technical conversations with client architects, R&D directors, and CIOs

Must-Haves :

- 8-12 years of software engineering experience, with at least 3-4 years in AI/ML systems

- Production knowledge graph experience : designed schemas, written non-trivial Cypher/Gremlin/SPARQL, operated Neo4j, Amazon Neptune, or TigerGraph at meaningful scale

- Strong Python and modern AI tooling : LangGraph, LangChain, or equivalent agent orchestration frameworks

- Hands-on experience with KG-augmented AI : GraphRAG, ontology-driven reasoning, or KG-augmented LLM retrieval

- Daily, fluent use of AI coding tools - Claude Code, Cursor, Kiro, GitHub Copilot, or equivalent. You should be able to demonstrate how you use these tools to ship production code, including prompting strategy, multi-file refactoring, agentic workflows, and where you trust them vs. override them

- Enterprise software shipping experience : data governance, lineage, multi-tenancy, observability