> Markdown version of [/jobs/ext/3965622-knowledge-graph-agentic-ai-architect](https://www.wearedevelopers.com/jobs/ext/3965622-knowledge-graph-agentic-ai-architect). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Knowledge Graph Agentic AI Architect - **Company:** Peoplentech Llc - **Location:** Alexandria, VA, United States - **Salary:** $130,000.0 - **Contract:** Temporary to permanent - **Skills:** LangGraph Framework, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Data Analysis, Microsoft Azure, Cloud Engineering, Databases, Data Architecture, Information Engineering, Data Governance, Extract Transform Load (ETL), Dataspaces, Data Systems, Data Warehousing, Entity Relationship Models, Graph Database, Python (Programming Language), PostgreSQL, Meta-Data Management, Microsoft SQL Server, MySQL, Neo4j, Performance Tuning, Query Optimization, Software Tools, Cloud Services, SQL Databases, Enterprise Data Management, Data Ingestion, Ontology Design, Retrieval-Augmented Generation, Large Language Models, Snowflake, Multi-Agent Systems, Indexer, Agentic-AI, Data Lakes, Information Technology, CrewAI, AutoGen, Machine Learning Operations, Graph RAG, Semantic Kernel - **Published:** October 27, 2026 - **Apply:** https://www.careerbuilder.com/job-details/knowledge-graph-agentic-ai-architect-alexandria-va--57d576b2-3cfb-4d83-8af4-a7f2e94443b0 ## About the Role Artificial Intelligence (AI), Artificial Intelligence (AI), Consulting, DataArchitect Data Modeling Tool, Automation, Business Processes, Ecosystems, SQL (Structured Query Language), Neo4j, Microsoft SQL Server, * Bachelor's or Master's degree in Computer Science, Data Engineering, AI, or a related field. * 12+ years of experience in Data Architecture, AI/ML, Enterprise Data Platforms, or Data Engineering. * Strong expertise in SQL database architecture, data modeling, query optimization, indexing, ETL/ELT design, and data warehousing. * Hands-on experience with Knowledge Graphs, graph databases, ontology design, and semantic technologies. * Experience with LLMs, vector databases, RAG architectures, and Agentic AI frameworks such as LangGraph, CrewAI, AutoGen, or Semantic Kernel. * Proficiency in Python, SQL, APIs, cloud services (AWS, Azure, or GCP), and modern data engineering tools. * Strong understanding of enterprise architecture, data governance, and AI solution design. Preferred Skills * Experience with Life Sciences, Healthcare, or other regulated industries. * Familiarity with MLOps, LLMOps, and AI monitoring frameworks. * Strong stakeholder management and technical leadership skills. Success Criteria * Successful delivery of scalable Knowledge Graph, Agentic AI, and enterprise data platforms. * Improved knowledge discovery, automation, analytics, and decision intelligence. * Adoption of enterprise-wide data and AI architecture standards and best practices. ## Description We are seeking an experienced Knowledge Graph, Agentic AI & Data Architect to design and deliver intelligent, scalable AI and data solutions. The ideal candidate will lead the architecture of knowledge graph platforms, Agentic AI systems, enterprise data platforms, and SQL-based data ecosystems to enable advanced search, reasoning, analytics, and business process automation., * Design and implement enterprise Knowledge Graph solutions using Neo4j, Amazon Neptune, or similar graph technologies. * Architect scalable relational and analytical data platforms using SQL Server, PostgreSQL, MySQL, Snowflake, or cloud-native databases. * Develop ontologies, semantic models, taxonomies, and entity relationship frameworks. * Design Agentic AI solutions leveraging LLMs, multi-agent frameworks, RAG, Graph RAG, and autonomous workflows. * Define enterprise data architecture, including data lakes, warehouses, vector databases, and metadata management. * Build scalable data ingestion, transformation, enrichment, and knowledge extraction pipelines. * Establish standards for data governance, security, performance optimization, observability, and AI reliability.