Graph AI Platform Engineer

Graph AI Platform Engineer Full-Time | On-Site | Dallas, TX —————————————————————————— ABOUT THE COMPANY —————————————————————————— A leading graph AI and knowledge engineering company, this organization sits at the intersection of connected data, machine intelligence, and semantic reasoning. They build sophisticated graph-powered platforms that help enterprises unlock relationships hidden within complex, large-scale data. With a strong engineering culture and a focus on deep technical excellence, this is a place where specialists thrive. —————————————————————————— THE ROLE —————————————————————————— This is a highly specialized platform engineering position for someone who has spent their career going deep on graph technologies — not a generalist AI role, and not a stepping stone. The Graph AI Platform Engineer will own the design and evolution of graph database infrastructure, bring machine learning capabilities into the graph layer, and establish the architectural standards that the broader engineering organization builds upon. If you have production-grade graph database experience, a genuine understanding of graph ML, and the seniority to set technical direction, this role was built for you. —————————————————————————— WHAT YOU WILL DO —————————————————————————— • Architect and maintain scalable graph database platforms in production environments, ensuring reliability, performance, and long-term extensibility • Design and enforce graph data modeling standards, including ontologies and semantic schemas that reflect real-world domain complexity • Develop and deploy Graph Neural Network models using frameworks such as DGL or PyTorch Geometric, integrating them into live platform workflows • Author and optimize complex graph queries, leveraging GSQL or equivalent languages to support analytical and operational use cases • Build and refine graph embedding pipelines and representation learning approaches that power downstream AI and search capabilities • Define graph architecture patterns and best practices that serve as the foundation for platform-wide engineering decisions • Collaborate with data scientists, ML engineers, and domain experts to translate business problems into graph-native solutions • Evaluate emerging graph AI techniques and selectively introduce them where they deliver measurable platform value —————————————————————————— WHAT WE ARE LOOKING FOR —————————————————————————— • 8+ years of professional software or data engineering experience • Production deployment experience with Neo4j, TigerGraph, or both • Proficiency in GSQL or a comparable graph query language • Hands-on experience building and training Graph Neural Networks • Practical knowledge of DGL or PyTorch Geometric in applied settings • Graph embedding and representation learning experience • Ontology and semantic modeling skills, including RDF and OWL • Demonstrated ability to own and drive graph architecture decisions The ideal candidate is an engineer who has grown up in the graph database world — someone whose instinct is to reach for a graph before a relational model, and who understands why that choice matters. This role demands more than query-writing ability; it requires the depth to design systems that are semantically coherent, performant at scale, and extensible as data complexity grows. A background in knowledge engineering, fraud and risk systems, cybersecurity infrastructure, or enterprise knowledge management will translate particularly well. Whether your path has been through data engineering with a machine learning lean, or through research-adjacent work on knowledge graphs, what matters most is that graph technology has been central to your career — not peripheral to it. —————————————————————————— NICE TO HAVE —————————————————————————— • Experience with GraphRAG architectures or retrieval patterns over graph stores • Hands-on integration of generative AI tooling with graph database backends • Exposure to knowledge graph applications in risk, fraud, or cybersecurity domains • Familiarity with enterprise ontology governance or semantic layer management Candidates who have explored the frontier where large language models meet structured graph knowledge will find this environment particularly stimulating. GraphRAG and generative AI integration with graph databases are active areas of interest for the platform, and engineers who have experimented in this space — even outside of a formal production context — bring a perspective that accelerates the team's roadmap. These capabilities are not required on day one, but they signal the kind of intellectual curiosity and forward orientation that fits the culture here. —————————————————————————— WHAT WE OFFER —————————————————————————— • On-site role based in Dallas, TX, embedded within a focused and technically rigorous engineering team • The opportunity to set architectural direction rather than execute someone else's blueprint • Exposure to some of the most complex and interesting graph AI problems in the enterprise space • A specialist environment where deep expertise is recognized, valued, and developed further • Clear pathways to grow into principal or staff engineering leadership as the platform scales • Collaboration with a team that takes knowledge engineering and graph ML seriously as disciplines — not as buzzwords

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