Principal Knowledge Automation Analyst

Job Requisition ID # 26WD97787 Position Overview Autodesk’s Technical Advisory organization is building a scalable knowledge platform that transforms implementation expertise from enterprise engagements into structured, reusable, and customer-facing guidance within Workflow Advisory. The Principal Knowledge Acquisition Analyst is responsible for designing and operating the systems that capture and transform knowledge from consulting engagements. This includes building AI-powered extraction and transformation pipelines and ensuring raw implementation data is converted into structured, template-aligned outputs ready for downstream content production. Reporting to the Senior Manager, Content & Knowledge, you will operate at the intersection of consulting delivery, data, and AI systems. You will own the upstream knowledge pipeline—from extracting implementation intelligence to delivering high-quality structured inputs aligned to the content model. In the first year, you will establish scalable AI-assisted capture and transformation pipelines and ensure knowledge is reliably converted into reusable, high-quality outputs. Responsibilities Knowledge Capture and AI Pipelines 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 key entities, relationships, and ontology governance required to organize and retrieve implementation knowledge at scale Manage and optimize AI agents, including prompt design, evaluation, and performance tuning Define content-type-specific chunking strategies for consulting artifacts and work with Engineering to implement retrieval-ready knowledge structures Define requirements and p articipate in embedding and vectorization evaluation to ensure captured knowledge can be effectively discovered through semantic search and AI-powered retrieval Define and track quality metrics (accuracy, completeness, error rates) and continuously improve pipeline performance Ensure secure handling of sensitive information, including automated redaction and compliance with governance standards Knowledge Transformation and Structured Ingestion Design and operate pipelines that convert raw, unstructured inputs into structured, template-aligned outputs using AI Map extracted knowledge to defined content model fields, ensuring outputs are complete, consistent, and production-ready Define structured capture methods (forms, schemas, workflows) to ensure key context (decisions, constraints, trade-offs) is captured Normalize and standardize data across sources and identify gaps to improve capture and transformation processes Define entity resolution and canonicalization rules to ensure concepts, terminology, and implementation knowledge are consistently represented across sources Quality, AI Readiness and Integration Ensure structured outputs support AI-driven use cases including vector search, retrieval-augmented generation (RAG), knowledge graph navigation, and downstream content generation Partner with the Content Model Lead to align transformation outputs with templates and structures Collaborate with Architecture and Engineering to align knowledge models, retrieval pipelines, and platform data models Define evaluation criteria for retrieval effectiveness, semantic relevance, and answer quality, and continuously improve knowledge performance through measurement and experimentation Minimum Qualifications 8+ years of experience in knowledge management, information architecture, information systems, semantic technologies, data engineering, or related field Experience working with AI/LLM-based workflows in production Experience designing data pipelines, structured capture, or transformation processes Strong analytical skills with ability to define and improve quality metrics Experience working cross-functionally with product, engineering, and domain experts Experienced in using technologies such as Knowledge management platforms: Confluence, SharePoint, Notion Enterprise, Gainsight Knowledge, Guru AI / LLM : OpenAI / AzureOpenAI , Anthropic Claude, Google Gemini Agentic Workflow & Orchestration: ReAct (Reason + Act), Chain of Thought ( CoT ) patterns AI Operations: Prompt design and evaluation, LLM output evaluation and benchmarking, Model monitoring and QA, RAG evaluation frameworks (e.g., RAGAS), retrieval observability tools (e.g., LangSmith , TruLens ), Vector Databases, Markdown files Knowledge Modeling & Semantic Technologies: Taxonomy and ontology management platforms (e.g., Semaphore, PoolParty ), knowledge graphs, entity resolution and semantic enrichment tools, graph exploration platforms (e.g., Neo4j Bloom) AI Extraction & Document Processing: , LlamaParse , document intelligence and content extraction platforms Cloud environments: Azure, AWS, Google The Ideal Candidate Has built AI-driven knowledge capture or data pipelines at scale Can transform messy, real-world data into structured, usable outputs Thinks systemically about data, content, and downstream use Works effectively across technical and non-technical teams Focuses on building practical, scalable systems #LI-AS1 Learn More About Autodesk Welcome to Autodesk! 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