Graph AI Platform Engineer

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

Look for: this is not a GenAI generalist role — don't try to fill it with the same candidate pool as the other two.

search for "Neo4j," "TigerGraph," "knowledge graph," "GNN" — treat as a distinct niche search, not a GenAI keyword search

• 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