Agentic AI / Semantic Solutions Architect

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
• 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 GCP 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