Agentic AI/Semantic Solutions Architect

Job Title : Agentic AI/Semantic Solutions ArchitectLocation : Atlanta, GA (Hybrid)Duration: Long term contractJob Description13+years of experienceWe are seeking a highly skilled Agentic AI / Semantic Solutions Architect to design and prototype advanced agent-layer architectures that operate on enterprise semantic data platforms. This role sits at the intersection of LLM orchestration, knowledge graphs, and semantic data modeling, focusing on building POC-level intelligent agent solutions rather than production-scale systems.The ideal candidate will have deep expertise in agent-based AI systems, GraphRAG architectures, and context engineering, with the ability to design frameworks where autonomous agents can effectively interpret and reason over structured knowledge.Key ResponsibilitiesArchitect and design agentic AI workflows that consume outputs from semantic layers, including knowledge graphs, ontologies, and metadata catalogsDevelop and prototype GraphRAG pipelines that combine graph traversal with vector-based retrieval for accurate, domain-grounded responsesDefine and implement context engineering strategies, including metadata injection, chunking, and semantic optimization for LLM promptsDesign and build Model Context Protocol (MCP) server patterns to enable seamless interaction between agents and semantic data systemsDevelop LLM orchestration workflows using frameworks such as LangChain, LangGraph, LlamaIndex, or AutoGenBuild pipelines for automated metadata extraction and semantic tagging using NLP and LLM-based approachesCollaborate with Semantic Data Architects to ensure ontologies and graph structures are optimized for agent traversal and queryingPrototype agent-based solutions for business use cases such as: Credit risk analysisCustomer data onboarding workflows Mandatory SkillsStrong expertise in Agentic AI architecture (multi-agent systems, tool usage, planning loops)Hands-on experience with GraphRAG design (hybrid graph + vector retrieval systems)Experience in LLM orchestration frameworks: LangChain, LangGraph, LlamaIndex, or AutoGenDeep understanding of context engineering techniques (chunking, windowing, semantic compression)Experience designing and integrating Model Context Protocol (MCP)Strong knowledge of semantic systems such as:Knowledge graphsOntologiesMetadata-driven architectures Nice To Have SkillsExperience with Google Vertex AI (Agent Builder / Search)Knowledge of Google Cloud Platform Spanner GraphFamiliarity with metadata platforms like Collibra or Google DataplexExperience with vector databases: Pinecone, Weaviate, pgvector, Vertex AI Vector SearchPrior experience in regulated domains such as financial services or legal systemsThanks & RegardsSaravananDMinds Solutions Inc.