Senior Knowledge Engineer

Knowledge ArchitectYou are a Knowledge Architect at the intersection of semantic AI and agentic systems — shaping the knowledge backbone of AI platforms by designing the ontologies, graphs, and data models that enable intelligent agents to reason, plan, and act. You are equally comfortable whiteboarding an ontology with a domain expert and pushing graph schemas to production alongside an ML team. You see the world as a graph, and you believe that well-structured knowledge is the foundation of truly intelligent machines. You thrive on translating complex, messy real-world knowledge into clean, reasoned, machine-readable structures that AI agents can act on — and you bring the rigor, curiosity, and collaboration to do it at scale.The WorkYou will embed directly with clients as a trusted technology advisor and hands-on engineer — leading the architecture and development of knowledge graphs, ontologies, and semantic data models that power next-generation agentic AI systems at enterprise scale.ResponsibilitiesOwn the end-to-end design, governance, and maintenance of enterprise-scale knowledge graphs and ontologies, bridging structured domain knowledge with large-scale agentic AI pipelines to enable reasoning, planning, and decision-making.Develop and govern ontologies, taxonomies, and semantic data models that formalize domain knowledge and support interoperability across systems and teams.Define and enforce data modeling standards, schema design patterns, and best practices for structured and semi-structured knowledge representation.Translate complex domain knowledge from subject matter experts into formal, machine-readable knowledge structures using RDF, OWL, SPARQL, or property graph models.Lead knowledge engineering discovery workshops and working sessions with client stakeholders to surface, validate, and formalize domain knowledge requirements.Collaborate with AI/ML engineers to integrate knowledge graphs as grounding and context layers for LLM-based agentic pipelines and retrieval-augmented generation (RAG) systems.Design knowledge structures that support multi-step agent reasoning, tool use, and dynamic planning across heterogeneous data sources.Work with project teams, team leaders, delivery leads, and client stakeholders to create standout Data & AI offerings powered by graph-based technologies.Collaborate with data engineering and platform teams to build scalable pipelines for knowledge graph population, enrichment, and lifecycle management.Develop strong client relationships and earn the trust of key stakeholders as a strategic advisor.Communicate complex ontological concepts and graph architectures clearly to both technical and non-technical audiences.Evaluate and pilot emerging tools, frameworks, and standards (e.g., LPG vs. RDF, Wikidata, schema.org, W3C standards).Required SkillsKnowledge Representation & OntologyOntology design and engineering (OWL, RDF, RDFS)Taxonomy and thesaurus developmentSemantic modeling and linked data principlesSchema design (schema.org, custom domain schemas) and W3C standardsKnowledge Graph TechnologiesProperty graph and RDF graph modelingGraph databases: Neo4j, Amazon Neptune, TigerGraph, StardogSPARQL, Cypher, and Gremlin query languagesGraph traversal, reasoning, inference, entity resolution, and enrichmentAgentic AI & LLM IntegrationRetrieval-Augmented Generation (RAG) architecturesLLM grounding and context design using structured knowledgeAgentic pipeline design: LangChain, LlamaIndex, AutoGenPrompt engineering for knowledge-intensive, enterprise-scale applicationsNeuro-symbolic AI concepts and reasoning frameworksData Modeling & EngineeringConceptual, logical, and physical data modelingGraph schema design and lifecycle managementEntity linking, disambiguation, and deduplicationMetadata management and data governanceProgramming & ToolingPython (primary); graph libraries: NetworkX, RDFLib, PyKEENSPARQL and graph query optimizationREST APIs, microservices integration, Git, CI/CD familiarityCloud platforms: AWS, Azure, GCPLocation & TravelThis is a hybrid role based in Dallas, TX, requiring 3 days per week in office. Qualified candidates in Columbus, OH; Tampa, FL; Atlanta, GA; and Houston, TX will also be considered.Travel is required and will vary between 25%–75% depending on business need and client requirements.Here’s what you needBachelor's degree or equivalent (minimum 12 years' work experience). Associate's degree requires minimum 6 years' equivalent work experience.4+ years of experience in Knowledge Graph technologies (e.g., RDF, SPARQL, Gremlin, LPG, SHACL, RDFS)2+ years of experience with schema design, ontology management, and Knowledge Graph curation2+ years of experience in semantic modeling and linked data principles2+ years of experience designing and developing knowledge graph solutions and graph-based machine learning models2+ years of experience with relational databases, object stores, graph databases (e.g., Stardog, Neo4j, Amazon Neptune, TigerGraph), and vector databases2+ years of experience in agentic pipeline design (LangChain, LlamaIndex, AutoGen)Preferred Qualifications2+ years of hands-on experience with cloud platforms (AWS, Azure, GCP)2+ years of experience in Python, with frameworks like TensorFlow, PyTorch, and ETL pipeline tools (e.g., Apache NiFi, Airflow)Practical experience with NLP and/or enterprise search techniquesPrompt engineering and LLM experience for enterprise-scale applicationsStrong cross-functional collaboration skills across engineering, research, and product teams in multiple time zonesPh.D. in Computer Science, Electrical Engineering, Mathematics, or a related fieldBroad experience in diverse ML techniques and agentic systems Accenture