Principal AI Knowledge AI Architect

We are seeking a Principal AI Knowledge AI Architect to design and lead the architecture for next-generation knowledge and RAG systems that enable reasoning-driven AI assistants to deliver precise, contextually relevant answers at scale. This role focuses on advanced RAG pipelines leveraging ontologies, dynamic content ingestion, agentic retrieval, data synchronization with enterprise platforms, and continuous knowledge health monitoring to ensure high-fidelity, trustworthy knowledge delivery. You will architect the knowledge layer of our AI Agentic Platform, integrating enterprise content repositories, knowledge bases, APIs, and external tools into an agent-driven reasoning and retrieval engine. Key Responsibilities: Advanced RAG ArchitectureArchitect and implement multi-layer RAG pipelines leveraging ontologies, semantic graphs, embeddings, and hybrid retrieval strategies. Design agentic RAG workflows where autonomous agents reason about query decomposition, multi-hop retrieval, and context stitching for better factual accuracy. Build hierarchical and ontology-based knowledge graphs to improve entity resolution, semantic search, and contextual reasoning. Optimize retrieval for domain-specific knowledge using structured + unstructured data fusion. Knowledge Ingestion & SynchronizationLead development of content ingestion pipelines for enterprise sources (Confluence, SharePoint, Google Drive, Salesforce KB, ServiceNow KB, etc.)Design real-time data sync connectors and ETL frameworks to keep knowledge sources fresh and in sync with external systems. Implement document parsing, enrichment, chunking, metadata tagging, and semantic indexing pipelines at scale. Agentic Knowledge & Reasoning IntegrationArchitect agentic knowledge workflows where agents autonomously evaluate, retrieve, and cross-reference multi-source knowledge. Enable agents to invoke external APIs/tools dynamically to complement RAG with transactional or dynamic information retrieval. Integrate multi-modal RAG (text, images, tables, PDFs) into reasoning loops for richer AI responses. Knowledge Quality & Health MonitoringDevelop knowledge health check pipelines to automatically validate knowledge freshness, detect stale or redundant articles, and recommend updates.Implement automated knowledge evaluation using LLMs (hallucination detection, coverage analysis, answer accuracy). Define governance policies for knowledge versioning, lifecycle management, and auditing.Scalability, Security & ComplianceArchitect multi-tenant, enterprise-ready knowledge systems with strict access controls, encryption, and compliance (SOC2, HIPPA, GDPR). Ensure cost-efficient vector database and embedding management strategies (e.g., partitioning, caching, tiered storage). Thought Leadership & CollaborationMentor engineers on best practices for RAG pipelines, knowledge representation, and semantic search. Work with product leadership to define long-term knowledge strategy for powering enterprise-grade agentic AI assistants.Collaborate closely with LLM engineers on optimizing retrieval-planning-generation loops for factual accuracy and latency. Please note: This is a hybrid role that will be based in San Mateo, CA, or Bellevue, WA, and requires an in-office presence three days per week (Tuesday - Thursday).