Agentic AI/Semantic Solutions Architect

Job Title : Agentic AI/Semantic Solutions Architect

Location : Atlanta, GA (Hybrid)

Duration: Long term contract

Job Description:

• 13 years of experience
• 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:
• Credit risk analysis
• 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:
• 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:
• Knowledge graphs
• Ontologies
• Metadata-driven architectures 

Nice to Have Skills

• Experience with Google Vertex AI (Agent Builder / Search)
• Knowledge of Google Cloud Platform Spanner Graph
• Familiarity with metadata platforms like Collibra or Google Dataplex
• Experience with vector databases:
• Pinecone, Weaviate, pgvector, Vertex AI Vector Search
• Prior experience in regulated domains such as financial services or legal systems

Thanks & Regards

Saravanan

DMinds Solutions Inc.