Principal Knowledge Automation Analyst

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 participate 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: Unstructured.io, 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 

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