Key Responsibilities and Required Skills for Knowledge Management Architect

🎯 Role Definition

We are seeking an experienced Knowledge Management Architect (KM Architect) to design and operationalize the organization’s knowledge strategy, information architecture, and AI-augmented knowledge services. The KM Architect will partner with product, engineering, data science, legal, and business stakeholders to build searchable, governed, and actionable knowledge bases using taxonomies, ontologies, knowledge graphs, semantic search, vector embeddings and LLM-enabled retrieval (RAG). This is a hands-on leadership role that blends technical design, vendor selection, data modeling, and change management to deliver measurable improvements in findability, time-to-answer, and knowledge reuse.

📈 Career Progression

Typical Career Path

Entry Point From:

Knowledge Manager / Senior Knowledge Manager
Information Architect or Taxonomy Lead
Data Architect / Data Engineer with KM responsibilities
Senior Content Strategist or Content Operations Lead
NLP/ML Engineer moving into applied knowledge systems

Advancement To:

Head / Director of Knowledge Management
Director of Information Architecture or Head of Content Platforms
VP of Data, Knowledge or Search
Chief Knowledge Officer or Chief Data & Analytics Officer

Lateral Moves:

Taxonomy & Ontology Lead
Product Manager for Search/AI products
Knowledge Graph Engineer or Semantic Search Lead
AI Solutions Architect focused on LLM + Retrieval

Core Responsibilities

Primary Functions

Develop and own the enterprise-level knowledge management strategy and roadmap that aligns information architecture, taxonomy, ontology, and knowledge graph initiatives with corporate objectives, customer experience goals, and ROI targets for search and support use cases.
Architect and implement comprehensive taxonomy and ontology solutions, including hierarchical taxonomies, faceted metadata schemes, controlled vocabularies, and relationship models that enable content discoverability, automated tagging, and semantic inference across domains.
Design, build and maintain knowledge graphs and entity models (property-graph or RDF) to represent business entities, relationships and lineage using GraphDB, Neo4j, RDF/OWL or comparable technologies to support semantic queries and advanced reasoning.
Lead the design and deployment of semantic search, hybrid search (keyword + vector), and ranking strategies using Elasticsearch, Opensearch, Solr, Pinecone, Milvus, FAISS or managed vector services, optimizing for precision, recall and answer quality.
Lead integration of LLMs, vector embeddings and Retrieval-Augmented Generation (RAG) pipelines into chatbots, virtual assistants, and internal search tools; define prompts, grounding strategies, context windows and hallucination mitigation processes.
Create and operationalize knowledge ingestion pipelines (ETL/ELT) that extract, normalize, deduplicate and enrich content from disparate sources (CRM, ERP, Confluence, SharePoint, ticketing systems, wikis, file stores) while maintaining provenance and auditability.
Define and enforce governance, lifecycle, and quality assurance policies for knowledge assets: authoring guidelines, review cadence, retention schedules, sensitivity classification, and escalation workflows for stale or inaccurate content.
Design metadata strategies and automated tagging pipelines using NLP (NER, entity linking, topic modeling), machine learning classifiers and rules-based enrichment to ensure consistent searchable metadata across structured and unstructured sources.
Implement role-based access controls, data protection and compliance measures for knowledge stores (PII masking, GDPR/CCPA considerations, enterprise IAM) and collaborate with security and legal teams to ensure safe LLM usage.
Run vendor evaluations, RFPs and POCs for knowledge platforms, search engines, embedding stores and KM tools; produce TCO analysis, integration plans and procurement recommendations.
Establish and monitor KPIs and analytics for knowledge performance (search success rate, time-to-answer, task completion, deflection rate, content coverage, response latency) and drive iterative improvements based on data.
Create developer-friendly APIs and microservices for knowledge retrieval, reasoning and entity resolution so product teams can seamlessly embed knowledge capabilities into customer-facing and internal applications.
Plan and execute migration strategies from legacy knowledge bases to modern platforms, including mapping taxonomies, transforming schemas, reconciling duplicates, and conducting stakeholder acceptance testing to minimize operational disruption.
Build and manage cross-functional programs for knowledge adoption: stakeholder mapping, training curriculums, content owner onboarding, success metrics and internal marketing to drive sustained usage and content hygiene.
Lead testing and validation frameworks for knowledge quality: relevance evaluation, search result sampling, user feedback loops, A/B testing of ranking algorithms and regression testing for model drift.
Design and operate monitoring and observability for knowledge services including search logs, query analytics, embedding drift checks, LLM response audits and anomaly detection to maintain trust and reliability.
Drive semantic enrichment projects—entity resolution, canonicalization and concept linking—to reconcile disparate vocabularies and create a single source of truth for organizational knowledge.
Document robust architecture diagrams, integration patterns, runbooks, data lineage documentation and incident response plans so technical teams can maintain and extend knowledge platforms efficiently.
Lead, mentor and grow a blended team of knowledge engineers, taxonomists, data scientists, content strategists and platform engineers; manage hiring, performance goals and career development for the KM organization.
Partner with UX and product teams to design information architectures and UI/UX patterns (facets, filters, answer cards, knowledge panels) that surface authoritative content and reduce time-to-resolution for end users.
Stay current on emerging technologies and best practices in knowledge management, semantic technologies, LLMs, embeddings and information retrieval; proactively recommend pilots and roadmap updates to executive leadership.

Secondary Functions

Support ad-hoc data requests and exploratory data analysis.
Contribute to the organization's data strategy and roadmap.
Collaborate with business units to translate data needs into engineering requirements.
Participate in sprint planning and agile ceremonies within the data engineering team.
Provide expertise during cross-functional incident investigations related to knowledge or search outages.
Develop training materials, developer guides and governance playbooks to enable content owners and platform integrators.
Participate in vendor management and contractual negotiations for KM and AI services.
Support audit readiness and compliance reporting for knowledge lifecycle and access controls.

Required Skills & Competencies

Hard Skills (Technical)

Enterprise knowledge strategy and information architecture design (taxonomy, ontology, content modeling).
Search technologies: Elasticsearch, OpenSearch, Solr and experience tuning ranking, analyzers and relevance.
Semantic search and vector search technologies: FAISS, Milvus, Pinecone, Weaviate or managed embedding services.
LLM integration and Retrieval-Augmented Generation (RAG) design patterns; prompt engineering and hallucination mitigation strategies.
Knowledge graph design and query languages: Neo4j, RDF/OWL, SPARQL, Cypher and graph data modeling.
NLP and text processing: named entity recognition (NER), entity linking, topic modeling, embeddings and transformer-based models.
Data integration and ETL pipelines: experience with Kafka, Airflow, Talend, NiFi or comparable ingestion frameworks.
API design and microservices: REST/GraphQL APIs for knowledge services, authentication and versioning patterns.
Metadata management and governance: schema design, controlled vocabularies, taxonomy management tooling (PoolParty, Smartlogic, TopBraid).
Cloud platforms and managed services: AWS (S3, Lambda, OpenSearch Service), GCP (Vertex AI), or Azure (Cognitive Services), including security controls.
Programming and scripting: Python, SQL, and familiarity with ML tooling (PyTorch, TensorFlow, Hugging Face) for prototype models.
Experience with enterprise collaboration platforms (Confluence, SharePoint, ServiceNow, Jira) and migration approaches.
Observability and analytics for knowledge systems: Kibana, Grafana, custom dashboards and search telemetry analysis.
Data protection, access control and compliance experience (IAM, RBAC, GDPR/CCPA, data masking).

Soft Skills

Strategic thinker who translates business goals into technical KM roadmaps and tangible KPIs.
Strong stakeholder management and cross-functional collaboration with product, engineering, legal and business leaders.
Excellent written and verbal communication: able to produce architecture docs, executive summaries and training materials.
Leadership and people management: hiring, mentoring and scaling high-performance multi-disciplinary teams.
Problem-solver with strong analytical and decision-making ability under ambiguity.
Change agent who can drive adoption, training and culture change across global organizations.
Customer-oriented mindset focused on user experience, findability and measurable outcomes.
Attention to detail in taxonomy and data modeling balanced with an ability to prioritize for impact.

Education & Experience

Educational Background

Minimum Education:

Bachelor's degree in Computer Science, Information Science, Library & Information Science, Data Science, Linguistics, or related field.

Preferred Education:

Master’s degree in Information Science, Knowledge Management, Computer Science, ML/NLP, or Business Administration (MBA) with technical focus.

Relevant Fields of Study:

Information Science / Library & Information Science
Computer Science / Software Engineering
Data Science / Machine Learning / NLP
Linguistics / Computational Linguistics
Knowledge Management / Information Systems

Experience Requirements

Typical Experience Range: 7–15 years of progressive experience in knowledge management, information architecture, search, or data engineering roles at enterprise scale.

Preferred: 10+ years implementing KM programs or search/semantic platforms with 3+ years in a leadership/architect role, proven track record of delivering KM platforms, knowledge graphs or LLM-enabled solutions in production.