Agentic AI / Semantic Solutions Architect

Job Title : Agentic AI / Semantic Solutions Architect Location : Atlanta, Georgia, USA (Hybrid on site)

Experience : 13+Years Role Overview We 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 Responsibilities • Architect and design agentic AI workflows that consume outputs from semantic layers, including knowledge graphs, ontologies, and metadata catalogs • Develop and prototype GraphRAG pipelines that combine graph traversal with vector-based retrieval for accurate, domain-grounded responses • Define and implement context engineering strategies, including metadata injection, chunking, and semantic optimization for LLM prompts • Design and build Model Context Protocol (MCP) server patterns to enable seamless interaction between agents and semantic data systems • Develop LLM orchestration workflows using frameworks such as LangChain, LangGraph, LlamaIndex, or AutoGen • Build pipelines for automated metadata extraction and semantic tagging using NLP and LLM-based approaches • Collaborate with Semantic Data Architects to ensure ontologies and graph structures are optimized for agent traversal and querying • Prototype agent-based solutions for business use cases such as: o Credit risk analysis o Customer data onboarding workflows Mandatory Skills • Strong 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: o LangChain, LangGraph, LlamaIndex, or AutoGen • Deep understanding of context engineering techniques (chunking, windowing, semantic compression) • Experience designing and integrating Model Context Protocol (MCP) • Strong knowledge of semantic systems such as: o Knowledge graphs o Ontologies o Metadata-driven architectures Nice to Have Skills • Experience with Google Vertex AI (Agent Builder / Search) • Knowledge of GCP Spanner Graph • Familiarity with metadata platforms like Collibra or Google Dataplex • Experience with vector databases: o Pinecone, Weaviate, pgvector, Vertex AI Vector Search • Prior experience in regulated domains such as financial services or legal systems