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Overview
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 Architecture Architect 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 & Synchronization Lead 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 Integration Architect 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 Monitoring Develop 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 & Compliance Architect multi-tenant, enterprise-ready knowledge systems with strict access controls, encryption, and compliance (SOC2, HIPAA, GDPR). Ensure cost-efficient vector database and embedding management strategies (e.g., partitioning, caching, tiered storage). Thought Leadership & Collaboration Mentor 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 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). Qualifications
Required Qualifications 10+ years in software architecture, with at least 3+ years in AI-driven knowledge systems, RAG pipelines, or semantic search Deep expertise in retrieval techniques (vector search, hybrid search, ontology-based retrieval) and knowledge graph design Experience with ontology design and reasoning (OWL, SPARQL, etc.) for enterprise knowledge modeling Proven experience building RAG pipelines with LLMs (OpenAI, Anthropic, LLaMA, etc.) integrated into production systems Strong proficiency in Java & Python and AI/ML frameworks (LangChain, LangGraph, etc.) Knowledge of vector DBs (Pinecone, ElasticSearch, etc.) and graph DBs (Neo4j, etc.) Experience building enterprise knowledge ingestion frameworks from CMS/CRM/ITSM platforms (e.g., Salesforce, ServiceNow) Background in document parsing (OCR, PDFs, HTML), metadata enrichment, and semantic embeddings Expertise in scalable cloud-architecture (Kubernetes, event-driven microservices, streaming pipelines) Understanding of agentic AI frameworks (LangChain, LangGraph) and their integration with retrieval for reasoning Other Qualifications Familiarity with self-healing knowledge pipelines (auto-detection and repair of broken links, stale knowledge) Strong grounding in AI safety and governance for enterprise knowledge systems Contributes to open-source RAG or knowledge graph frameworks are a plus Familiarity with multi-modal knowledge retrieval (image/document embeddings and cross-modal search) Additional Information The annual base salary range for this position is $260,500 - $374,440. Compensation is based on a variety of factors including but not limited to location, experience, job-related skills, and level. Bonus/equity may be available. Freshworks offers multiple options for dental, medical, vision, and life insurances. Equity + ESPP, flexible PTO, flexible spending, commuter benefits and wellness benefits are also offered. Freshworks also offers adoption and parental leave benefits. At Freshworks, we are creating a global workplace that enables everyone to find their true potential, purpose, and passion irrespective of their background, and we are committed to providing equal opportunity for all and believe that in the workplace diversity creates a more vibrant, richer work environment that advances the goals of our employees, communities and the business.
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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 Architecture Architect 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 & Synchronization Lead 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 Integration Architect 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 Monitoring Develop 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 & Compliance Architect multi-tenant, enterprise-ready knowledge systems with strict access controls, encryption, and compliance (SOC2, HIPAA, GDPR). Ensure cost-efficient vector database and embedding management strategies (e.g., partitioning, caching, tiered storage). Thought Leadership & Collaboration Mentor 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 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). Qualifications
Required Qualifications 10+ years in software architecture, with at least 3+ years in AI-driven knowledge systems, RAG pipelines, or semantic search Deep expertise in retrieval techniques (vector search, hybrid search, ontology-based retrieval) and knowledge graph design Experience with ontology design and reasoning (OWL, SPARQL, etc.) for enterprise knowledge modeling Proven experience building RAG pipelines with LLMs (OpenAI, Anthropic, LLaMA, etc.) integrated into production systems Strong proficiency in Java & Python and AI/ML frameworks (LangChain, LangGraph, etc.) Knowledge of vector DBs (Pinecone, ElasticSearch, etc.) and graph DBs (Neo4j, etc.) Experience building enterprise knowledge ingestion frameworks from CMS/CRM/ITSM platforms (e.g., Salesforce, ServiceNow) Background in document parsing (OCR, PDFs, HTML), metadata enrichment, and semantic embeddings Expertise in scalable cloud-architecture (Kubernetes, event-driven microservices, streaming pipelines) Understanding of agentic AI frameworks (LangChain, LangGraph) and their integration with retrieval for reasoning Other Qualifications Familiarity with self-healing knowledge pipelines (auto-detection and repair of broken links, stale knowledge) Strong grounding in AI safety and governance for enterprise knowledge systems Contributes to open-source RAG or knowledge graph frameworks are a plus Familiarity with multi-modal knowledge retrieval (image/document embeddings and cross-modal search) Additional Information The annual base salary range for this position is $260,500 - $374,440. Compensation is based on a variety of factors including but not limited to location, experience, job-related skills, and level. Bonus/equity may be available. Freshworks offers multiple options for dental, medical, vision, and life insurances. Equity + ESPP, flexible PTO, flexible spending, commuter benefits and wellness benefits are also offered. Freshworks also offers adoption and parental leave benefits. At Freshworks, we are creating a global workplace that enables everyone to find their true potential, purpose, and passion irrespective of their background, and we are committed to providing equal opportunity for all and believe that in the workplace diversity creates a more vibrant, richer work environment that advances the goals of our employees, communities and the business.
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