Graph AI Platform Engineer - Neo4j/TigerGraph

SharpAtoms; Dallas (TX); On-site; USD 140,000 - 190,000; Full time

Job summary

SharpAtoms is seeking a Graph AI Platform Engineer to define and drive a specialized graph architecture. You will shape standards for production graph systems and graph ML workflows in a highly technical context.

The role emphasizes deep graph database expertise (Neo4j/TigerGraph), GNNs, RDF/OWL ontologies, and scalable knowledge management strategies within fraud/risk and security domains.

Qualifications

• 8+ years of senior graph/knowledge graph experience.
• Production graph database expertise with Neo4j and/or TigerGraph.
• Experience with GNNs and graph ML libraries such as DGL or PyTorch Geometric.
• Ontology/semantic modeling experience (RDF, OWL).

Skills

Neo4j

TigerGraph

knowledge graph

GNNs

Graph embeddings

DGL

PyTorch Geometric

RDF

OWL

GraphRAG

Tools

GSQL

PyTorch

DGL

Job description

Graph AI Platform Engineer — "Specialist, different skill tree entirely"

Looking for: this is not a GenAI generalist role. Need someone with "Neo4j," "TigerGraph," "knowledge graph," "GNN" — treat as a distinct niche role

• Deep graph database expertise: Neo4j and/or TigerGraph in production, GSQL or equivalent query languages
• Graph ML background: GNNs, DGL or PyTorch Geometric, graph embeddings/representation learning
• Ontology/semantic modeling experience (RDF, OWL) — this is closer to a knowledge engineering skillset than typical app dev
• GraphRAG exposure is a bonus, not a requirement — the graph expertise is the hard-to-find part; GenAI integration can be taught
• 8+ years signals they want someone senior enough to set architecture/standards, likely with prior work in fraud/risk/cybersecurity or knowledge management domains where graphs are common
• Good fit: a graph database/data engineer with ML leanings, or a research-adjacent engineer who's worked on knowledge graphs — much smaller, more specialized talent pool than the other two roles