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 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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About Autodesk

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Salary transparency

Salary is one part of Autodesk’s competitive compensation package. For Poland based roles, we expect a starting base salary between $187,500 and $275,000. Offers are based on the candidate’s experience and geographic location, and may exceed this range. In addition to base salaries, our compensation package may include annual cash bonuses, commissions for sales roles, stock grants, and a comprehensive benefits package.

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