Principal Knowledge Automation Analyst
Core
Designing and operating AI-powered pipelines to capture, transform, and structure knowledge from consulting engagements into reusable, template-aligned outputs for downstream content production and AI-driven retrieval.
Role type
Principal Knowledge Automation Analyst (AI/LLM pipelines & knowledge engineering)
Builds
Scalable AI-assisted capture and transformation pipelines, structured knowledge models, and retrieval-ready knowledge structures.
Domain
Professional services consulting, AI/LLM operations, knowledge management, semantic technologies.
Deliverable
production ML models | product features
Required skills
AI/LLM workflow design, data pipeline engineering, knowledge modeling & ontology governance, agentic workflow orchestration, prompt design & evaluation, vector database management, entity resolution & canonicalization, quality metric definition, cross-functional collaboration.
Preferred skills
Experience building AI-driven knowledge capture at scale, transforming messy real-world data into structured outputs, systematic thinking about data/content/downstream use.
Technologies
OpenAI, AzureOpenAI, Anthropic Claude, Google Gemini, ReAct, Chain of Thought, RAGAS, LangSmith, TruLens, Neo4j Bloom, Unstructured.io, LlamaParse, Confluence, SharePoint, Notion Enterprise, Gainsight Knowledge, Guru, Semaphore, PoolParty, Azure, AWS, Google.
Responsibilities
Design and operate AI-powered pipelines to capture knowledge from enterprise systems, meeting transcripts, and engagement artifacts; Define and evolve the conceptual knowledge model including entities, relationships, and ontology governance; Manage and optimize AI agents including prompt design, evaluation, and performance tuning; Define content-type-specific chunking strategies and work with Engineering to implement retrieval-ready knowledge structures; Define requirements and participate in embedding and vectorization evaluation for semantic search and AI-powered retrieval; Design and operate pipelines converting raw unstructured inputs into structured, template-aligned outputs; Ensure structured outputs support AI-driven use cases including vector search, RAG, and knowledge graph navigation.
Seniority
Principal, hands-on IC with strategic ownership of upstream knowledge pipelines.