Graph AI Platform Engineer

Evolve Esolutions LLC; Dallas (TX); On-site; USD 160,000 - 210,000; Full time

Job summary

Evolve Esolutions LLC seeks a senior Graph AI Platform Engineer to design and govern a specialized graph technology stack, distinct from GenAI generalist roles. The candidate will focus on production-grade graph databases (Neo4j/TigerGraph), graph neural networks, and ontology modeling within fraud/risk or knowledge-management contexts.

The role demands setting architecture standards, collaborating with ML engineers, and guiding a small expert team to advance graph representations and

Qualifications

• Deep graph database expertise with production experience in Neo4j or TigerGraph
• Graph ML background with GNNs, DGL or PyTorch Geometric
• Ontology/semantic modeling using RDF/OWL
• Knowledge engineering focus; graph expertise prioritized over GenAI generalism
• 8+ years signals seniority and architecture/standards leadership
• Background in fraud/risk/cybersecurity or knowledge management domains

Responsibilities

• Set architecture and engineering standards for the graph platform
• Lead design and implementation of graph ML models and embeddings
• Collaborate with ML and data teams to integrate graph representations into pipelines
• Evaluate and adopt GraphRAG approaches as a potential capability
• Mentor junior engineers and align stakeholders on graph technology roadmaps

Skills

Neo4j

TigerGraph

Knowledge graph

GNN

DGL

PyTorch Geometric

Graph embeddings

Ontology modeling

RDF

OWL

Tools

Neo4j

TigerGraph

GSQL

RDF

OWL

Job description

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