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 Google Cloud Platform 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