{"schemaVersion":"jobsearcher.job.v1","id":"b7f8666c52c0c29fe0bbffc8","url":"https://jobsearcher.com/jobs/b7f8666c52c0c29fe0bbffc8","canonicalUrl":"https://jobsearcher.com/jobs/b7f8666c52c0c29fe0bbffc8","title":"Graph Engineer","description":"Graph Engineer The Role We are looking for a Graph Engineer to design and build a knowledge graph that helps AI systems understand complex construction projects. This role is not about simply storing documents in a vector database. It is about modeling the relationships between tenders, specifications, drawings, RFQs, quotes, subcontractors, suppliers, materials, milestones, and regulatory requirements so AI agents can reason across project documentation in a structured and reliable way. You will work across graph databases, ontology design, document processing, vector retrieval, and AI systems to turn unstructured construction data into a queryable knowledge layer.\r\nWhat You'll Do Design the core ontology and graph schema for construction knowledge, including entities, relationships, node types, edge semantics, and property structures.\r\nBuild pipelines that convert raw documents such as PDFs, CAD files, spreadsheets, emails, specs, drawings, and quotes into structured graph entities.\r\nExtract entities, identify relationships, and resolve references across multiple documents.\r\nDesign efficient graph traversal patterns for agent queries, including questions such as \"What subcontractor quotes cover this specification?\" \"What penalties apply if this milestone is missed?\" and \"Which drawings, materials, and suppliers are connected to this requirement?\".\r\nCombine vector-based retrieval with graph-based reasoning by using semantic search alongside relationship traversal.\r\nOptimize graph performance at scale, including indexing, caching, incremental updates, and large-volume relationship management.\r\nSupport multilingual knowledge modeling across English, Dutch, and Japanese while maintaining semantic consistency.\r\nWhat We're Looking For Strong experience designing knowledge graphs from first principles in a complex domain such as construction, engineering, legal, medical, financial, insurance, or supply chain.\r\nProduction experience with Neo4j or similar graph databases such as Amazon Neptune, TigerGraph, or JanusGraph.\r\nStrong understanding of Cypher or Gremlin, graph query optimization, indexing, schema design, and memory management.\r\nExperience designing ontologies, taxonomies, or formal domain models.\r\nStrong understanding of RAG, vector embeddings, semantic search, and the limitations of vector-only retrieval.\r\nStrong Python skills and experience building scalable data pipelines for diverse document types.\r\nComfort working in cloud environments, preferably AWS.\r\nPreferred Experience Experience with construction, engineering, procurement, architecture, or other document-heavy technical domains.\r\nBackground in NLP, named entity recognition, entity extraction, relationship extraction, or document intelligence.\r\nExperience using LLMs for structured extraction and reasoning.\r\nExperience with graph neural networks, knowledge graph embeddings, or hybrid graph/vector retrieval.\r\nExperience processing PDFs, CAD files, technical drawings, contracts, spreadsheets, emails, and regulatory documents.\r\nTechnical Environment The current technical environment includes Neo4j, Cypher, Pinecone, ChromaDB, Python, unstructured.io, LlamaParse, custom OCR pipelines, Claude, GPT-4, Mistral, AWS, S3, RDS, ECS/EKS, PostgreSQL, Terraform, and CrewAI.\r\nIdeal Profile The ideal candidate is a hands-on graph engineer who can design the knowledge architecture behind AI systems, build reliable data pipelines, and create graph structures that support complex reasoning across highly interconnected construction documents.\r\nSalt is acting as an Employment Agency in relation to this vacancy.\r\nJ-18808-Ljbffr","company":"Salt Digital Recruitment","rawCompany":"salt digital recruitment","city":"Millbrae","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-07-04T02:34:22.135Z","occupations":[{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"15-1243.01","title":"Data Warehousing Specialists","slug":"data-warehousing-specialists"},{"code":"15-1243.00","title":"Database Architects","slug":"database-architects"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"},{"code":"541330","title":"Engineering Services","slug":"engineering-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Graph Engineer","description":"Graph Engineer The Role We are looking for a Graph Engineer to design and build a knowledge graph that helps AI systems understand complex construction projects. This role is not about simply storing documents in a vector database. It is about modeling the relationships between tenders, specifications, drawings, RFQs, quotes, subcontractors, suppliers, materials, milestones, and regulatory requirements so AI agents can reason across project documentation in a structured and reliable way. You will work across graph databases, ontology design, document processing, vector retrieval, and AI systems to turn unstructured construction data into a queryable knowledge layer.\r\nWhat You'll Do Design the core ontology and graph schema for construction knowledge, including entities, relationships, node types, edge semantics, and property structures.\r\nBuild pipelines that convert raw documents such as PDFs, CAD files, spreadsheets, emails, specs, drawings, and quotes into structured graph entities.\r\nExtract entities, identify relationships, and resolve references across multiple documents.\r\nDesign efficient graph traversal patterns for agent queries, including questions such as \"What subcontractor quotes cover this specification?\" \"What penalties apply if this milestone is missed?\" and \"Which drawings, materials, and suppliers are connected to this requirement?\".\r\nCombine vector-based retrieval with graph-based reasoning by using semantic search alongside relationship traversal.\r\nOptimize graph performance at scale, including indexing, caching, incremental updates, and large-volume relationship management.\r\nSupport multilingual knowledge modeling across English, Dutch, and Japanese while maintaining semantic consistency.\r\nWhat We're Looking For Strong experience designing knowledge graphs from first principles in a complex domain such as construction, engineering, legal, medical, financial, insurance, or supply chain.\r\nProduction experience with Neo4j or similar graph databases such as Amazon Neptune, TigerGraph, or JanusGraph.\r\nStrong understanding of Cypher or Gremlin, graph query optimization, indexing, schema design, and memory management.\r\nExperience designing ontologies, taxonomies, or formal domain models.\r\nStrong understanding of RAG, vector embeddings, semantic search, and the limitations of vector-only retrieval.\r\nStrong Python skills and experience building scalable data pipelines for diverse document types.\r\nComfort working in cloud environments, preferably AWS.\r\nPreferred Experience Experience with construction, engineering, procurement, architecture, or other document-heavy technical domains.\r\nBackground in NLP, named entity recognition, entity extraction, relationship extraction, or document intelligence.\r\nExperience using LLMs for structured extraction and reasoning.\r\nExperience with graph neural networks, knowledge graph embeddings, or hybrid graph/vector retrieval.\r\nExperience processing PDFs, CAD files, technical drawings, contracts, spreadsheets, emails, and regulatory documents.\r\nTechnical Environment The current technical environment includes Neo4j, Cypher, Pinecone, ChromaDB, Python, unstructured.io, LlamaParse, custom OCR pipelines, Claude, GPT-4, Mistral, AWS, S3, RDS, ECS/EKS, PostgreSQL, Terraform, and CrewAI.\r\nIdeal Profile The ideal candidate is a hands-on graph engineer who can design the knowledge architecture behind AI systems, build reliable data pipelines, and create graph structures that support complex reasoning across highly interconnected construction documents.\r\nSalt is acting as an Employment Agency in relation to this vacancy.\r\nJ-18808-Ljbffr","datePosted":"2026-07-04T02:34:22.135Z","dateModified":"2026-07-04T02:34:22.135Z","hiringOrganization":{"@type":"Organization","name":"Salt Digital Recruitment","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Millbrae","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"b7f8666c52c0c29fe0bbffc8"},"url":"https://jobsearcher.com/jobs/b7f8666c52c0c29fe0bbffc8"}}