Principal Knowledge Automation Engineer

on 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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