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Knowledge Architect

TechnicalFast GrowingHiring since 2024

Designs and governs enterprise AI knowledge infrastructure — building taxonomies, knowledge graphs, and RAG pipelines so AI retrieves accurate, trusted information. GraphRAG and agentic AI are driving new demand. Requires understanding both LLM internals and enterprise data governance — a crossover upgrade of Data Engineer and AI Engineer. Consulting firms and enterprise IT departments in Singapore and Australia are major APAC employers.

Salary Range
$100K–$170K
Hiring Companies
Palantir · Databricks · Snowflake
Hot Regions
USASingaporeAustraliaU.K.European Union
Core Skills
Knowledge graph · Enterprise data · RAG

Learn this at JR Academy

Real bootcamps currently running on jiangren.com.au — click through for the full outline and enrollment

AI Engineer Fundamentals: RAG BasicsEntry-level · Vector DB + Embedding + Retrieval pipeline
View Course →
Use RAG to Improve GenAI Responses WorkshopWorkshop · RAG hallucination control + retrieval tuning
View Course →
AI Engineer Bootcamp12 weeks · Intermediate · LangChain + RAG + Agent + Fine-tuning + LLM Ops
View Course →

Job Description Analysis

Core Responsibilities

  • Designing enterprise AI knowledge infrastructure—classification systems, knowledge graphs, ontologies, and metadata standards
  • Build and optimize the knowledge layer of the RAG pipeline: document governance, knowledge extraction, semantic indexing
  • Develop data quality standards and knowledge lifecycle management processes (create → review → publish → update → archive)
  • Evaluate and introduce knowledge management tools (graph databases, knowledge graph platforms, enterprise search engines)
  • Collaborate with various business units to ensure the knowledge used by AI systems is accurate, timely and trustworthy

Requirements

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  1. Deep understanding of knowledge graph technologies: RDF, OWL, SPARQL or Property Graph
  2. Experience in designing and optimizing RAG systems or enterprise search systems
  3. Master at least one graph database (Neo4j, Amazon Neptune, TigerGraph)
  4. 3+ years of data governance or information architecture experience
  5. Excellent cross-team communication skills and ability to understand the knowledge structure of different business areas
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Nice to Have

  • Library science or information science background
  • Learn about GraphRAG and knowledge-augmented retrieval techniques
  • Experience with enterprise-level data governance platforms (Collibra, Alation)

A Day in the Life

  • Morning: Review the weekly update report of the knowledge graph and check the quality of new entities and relationships
  • Morning: Discuss the taxonomy and metadata structure of the contract knowledge base with the legal team
  • Afternoon: Optimizing the retrieval effect of the RAG pipeline - adjusting entity linking and semantic disambiguation strategies
  • Afternoon: Evaluate the feasibility of the GraphRAG solution and discuss the architecture design with the engineering team
  • Evening: Update the enterprise knowledge classification system document and add the domain concepts of new business lines

Career Transition Guide

Suitable Backgrounds

  • Data Engineer: Familiar with data pipelines and governance processes, just supplement knowledge graph technologySee this career today: Data Engineer
  • Search engine engineer: Highly matched information retrieval capabilities, extended to knowledge management
  • Information Architect/Taxonologist: Enterprise knowledge classification experience is directly applicable
  • AI/NLP Engineer: Have a foundation in semantic understanding and the direction of transforming knowledge architecture

Transition Path

  1. Step 1: Learn the basics of knowledge graphs: nodes, relationships, attributes, and query language (Cypher/SPARQL)
  2. Step 2: Use Neo4j to build a domain knowledge graph (such as a skill tree or course relationship graph)
  3. Step 3: Learn the knowledge layer design in the RAG pipeline—document slicing, entity extraction, and semantic indexing
  4. Step 4: Research GraphRAG technology and practice knowledge graph-enhanced retrieval solutions
  5. Step 5: Build a complete enterprise knowledge management demo and apply for the position of Knowledge Architect

Learning Roadmap

Phase 1: Basics of Knowledge Engineering (January-February)

Learn the basic concepts of knowledge graph: entities, relationships, attributes, and ontologyMaster the Neo4j and Cypher query languagesUnderstand the basic framework of enterprise data governance and metadata management

Phase 2: AI + Knowledge (2-4 months)

Learn NLP entity recognition and relationship extraction techniquesMaster knowledge layer design and optimization in the RAG pipelinePractice GraphRAG: Knowledge graph-enhanced retrieval architecture

Phase 3: Enterprise-level practice (April-June)

Design enterprise-level knowledge classification system and life cycle management processBuild a knowledge quality monitoring and automated verification systemComplete an end-to-end project including knowledge graph + RAG

Common Mistakes

Wrong

Designing an overly complex ontology from the beginning, trying to cover all concepts

Right

Start with 20% high-frequency concepts in core business areas and gradually expand

Wrong

Only focus on the technical implementation of the knowledge graph and ignore the knowledge governance process

Right

Technology is just a tool - who produces knowledge, reviews it, and updates it is the key to long-term success

Wrong

Think that the knowledge architecture is a one-time project and will be ignored after it is built.

Right

Enterprise knowledge is alive – it needs to be maintained, updated and governed on an ongoing basis

Recommended Resources

AI Engineer Learning PathAI Data AnalysisPrompt Master FundamentalsAI Career Impact Map

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