Principal Knowledge Acquisition 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 Engineer 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
• Manage and optimize AI agents, including prompt design, evaluation, and performance tuning
• 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

• 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

• Ensure structured outputs support AI-driven use cases such as retrieval (RAG) 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 pipelines with platform data models
• Identify and resolve data quality issues and improve end-to-end system performance

• 8+ years of experience in knowledge management, data engineering, information systems, or related fields
• 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

• 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, Vector Databases, Markdown files
• Cloud environments: Azure, AWS, Google

• 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