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Key Responsibilities and Required Skills for Knowledge Management Engineer

💰 $ - $

Knowledge ManagementSearchData EngineeringAIInformation Architecture

🎯 Role Definition

We are seeking a proactive Knowledge Management Engineer to design, build, and optimize the systems and processes that help an organization find, trust, and act on the right knowledge at the right time. This role sits at the intersection of information architecture, search engineering, data engineering, and applied AI: you will architect knowledge bases and search experiences, implement metadata and taxonomy strategies, integrate LLMs and vector search where appropriate, and measure adoption and impact across product and support workflows. Ideal candidates combine hands-on technical skills (search platforms, ETL, APIs, embedding pipelines) with strong stakeholder management, content governance, and a product mindset.

Core keywords: Knowledge Management, enterprise search, knowledge base, taxonomy, ontology, semantic search, vector database, embeddings, RAG (retrieval-augmented generation), LLM integrations, information architecture, content lifecycle, knowledge governance.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Technical Writer or Documentation Engineer transitioning to systems-focused KM
  • Search Engineer, Search Relevance Engineer, or Search Developer
  • Data Engineer or BI Engineer with interest in content and metadata
  • Information Architect, Librarian, or Knowledge Analyst

Advancement To:

  • Senior Knowledge Management Engineer / Lead KM Engineer
  • Knowledge Manager / Head of Knowledge Operations
  • Director of Knowledge Management or Director of Information Architecture
  • Chief Knowledge Officer, Head of AI-assisted Knowledge, or Product Lead for Knowledge Platforms

Lateral Moves:

  • Information Architecture / UX Content Strategy
  • AI/ML Engineer focusing on semantic search and retrieval
  • Content Operations / Documentation Strategy
  • Enterprise Search or Relevance Engineering

Core Responsibilities

Primary Functions

  • Design, own, and evolve an enterprise knowledge architecture that supports search, chatbots, case deflection, and internal/external knowledge bases; define content models, taxonomies, ontologies, and metadata schemas to drive discoverability and trust.
  • Implement and maintain enterprise search and relevance stacks (e.g., Elasticsearch, OpenSearch, Coveo, Algolia, Google Cloud Search, Amazon Kendra) including index design, analyzers, ranking algorithms, and relevancy tuning to improve retrieval precision and recall.
  • Build and operate semantic search and vector retrieval pipelines using embedding models and vector databases (e.g., FAISS, Milvus, Pinecone, Weaviate) to support natural language search, similarity matching, and RAG-enabled experiences.
  • Integrate LLMs and RAG workflows into support and knowledge workflows (e.g., chat assistants, automated summarization, contextual answer generation); design prompt templates, retrieval strategies, and safety/guardrails for production use.
  • Develop scalable ingestion and ETL pipelines to collect knowledge from multiple sources (Confluence, SharePoint, Git, CRM, ticketing systems, wikis, internal databases) and normalize content with automated metadata enrichment, deduplication, and content classification.
  • Create and maintain taxonomy and controlled vocabularies; run content audits and classification projects to align tags, categories, and synonyms with business terminology and user search behavior.
  • Design and implement knowledge governance policies and processes, including retention rules, ownership and stewardship models, versioning, publishing workflows, access control, and regulatory compliance (e.g., data privacy and security considerations).
  • Partner with product, support, engineering, and content teams to define KPIs (search success rate, time-to-answer, case deflection, usage/adoption metrics) and instrument analytics to measure the health and impact of knowledge systems.
  • Lead complex migrations and consolidation projects to move legacy knowledge bases into modern platforms; create migration plans, mapping of schemas, rollback strategies, and verify data fidelity and search performance post-migration.
  • Create automated testing and quality assurance for knowledge artifacts and search behavior: relevance tests, click-through analysis, A/B experiments, synthetic queries, continuous monitoring, and alerting for regressions.
  • Implement content lifecycle automation (archival, review reminders, stale content detection) using rules and ML-driven scorers to keep the knowledge base current and reduce noise in search results.
  • Build APIs and microservices to expose normalized knowledge to internal and external applications (chatbots, support tools, product UIs), including auth, rate limiting, monitoring, and SLA design.
  • Design and maintain knowledge graphs and semantic models where appropriate (RDF, property graphs, Neo4j) to represent relationships between entities, processes, and content for improved context-aware retrieval and reasoning.
  • Collaborate with data engineering and security teams to ensure proper data lineage, provenance tagging, encryption, and access controls for knowledge assets that include sensitive or PII data.
  • Lead stakeholder interviews, user research, and search analytics reviews to iterate on search relevance, synonyms, query rewriting rules, and new discovery features that address real user pain points.
  • Provide technical leadership on vendor evaluations and procurement for search, KM, and AI tooling; create evaluation criteria, run proofs-of-concept, manage pilot implementations, and integrate third-party solutions into the tech stack.
  • Drive operational playbooks for knowledge operations, including incident response for search outages, change management for index updates, and runbooks for content steward workflows.
  • Create training materials, run workshops, and onboard content owners and support teams on best practices for writing discoverable content, tagging rules, and using knowledge tools effectively.
  • Optimize and fine-tune LLM safety, hallucination mitigation, and truthfulness controls by combining retrieval checks, source attribution, citation policies, and confidence thresholds into production pipelines.
  • Implement multilingual knowledge strategies, including translation workflows, locale-specific taxonomies, cross-language embeddings, and language detection to support global teams and international users.
  • Monitor and report on performance, adoption, and ROI; translate usage data into prioritized roadmaps and feature requests to continuously improve knowledge experiences.

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.
  • Maintain documentation of system architecture, data flows, and knowledge processes for cross-functional visibility and onboarding.
  • Assist in vendor/contractor coordination, managing SLAs and deliverables for external KM and AI partners.
  • Mentor junior engineers and knowledge analysts; establish best practices and code/QA standards for knowledge systems.
  • Support privacy reviews and legal/records requests related to knowledge assets and content lifecycle.

Required Skills & Competencies

Hard Skills (Technical)

  • Enterprise search platforms: Elasticsearch / OpenSearch, Algolia, Coveo, Amazon Kendra, Google Cloud Search (experience tuning indices, analyzers, scoring, and boosting).
  • Semantic search & vector retrieval: building embedding pipelines, familiarity with vector databases (FAISS, Pinecone, Milvus, Weaviate) and embedding models (OpenAI embeddings, SentenceTransformers).
  • LLM and RAG integration: experience integrating LLMs (OpenAI, Anthropic, Llama family, Azure OpenAI) with retrieval layers and prompt engineering for production.
  • Programming & scripting: Python (preferred), Node.js or equivalent for building ingestion jobs, microservices, and data processing.
  • Data engineering: ETL/ELT design, data normalization, queues, batch and streaming pipelines, and experience with tools like Airflow, dbt, or equivalent orchestration frameworks.
  • APIs and integrations: RESTful API design, graphQL familiarity, OAuth, webhooks, and experience with third-party connectors (Confluence, SharePoint, Zendesk, Salesforce, Git).
  • Knowledge modeling: taxonomy design, ontology modeling (SKOS, RDF), knowledge graph experience, metadata schema design.
  • Search relevance and evaluation: A/B testing, relevance metrics, query logs analysis, click-through and user behavior analytics.
  • Databases & infrastructure: SQL (Postgres, MySQL), NoSQL basics, experience deploying search/ML infra on cloud platforms (AWS, GCP, Azure) and containerization (Docker, Kubernetes).
  • DevOps & monitoring: CI/CD for knowledge pipelines, logging and observability, automated testing frameworks for search relevancy and data integrity.
  • Security & compliance: data privacy best practices, role-based access control, encryption-at-rest/in-transit, PII handling in knowledge systems.
  • Familiarity with documentation and content platforms: Confluence, SharePoint, Document360, Zendesk Guide, GitBook, or similar.
  • Experience with analytics and visualization tools: Looker, Tableau, Google Analytics, Kibana, or Metabase to derive insights from KM usage.

Soft Skills

  • Strong stakeholder management and cross-functional collaboration: able to translate business problems into technical solutions and rally product, support, and content teams.
  • Excellent written and verbal communication: create clear documentation, reports, and training material targeted to technical and non-technical audiences.
  • Product mindset and prioritization: balance quick wins and long-term investments to maximize ROI from KM initiatives.
  • Analytical problem-solving: use data to diagnose search failures, content gaps, and prioritize remediation.
  • Change management and training: able to run workshops, onboard users, and drive adoption of new knowledge workflows.
  • Attention to detail and quality orientation, especially around content hygiene, metadata, and governance.
  • Curiosity and continuous learning: keep current on semantic search, embeddings, LLM safety, and KM tooling trends.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor's degree in Computer Science, Information Science, Library Science, Data Science, Linguistics, Human-Computer Interaction, or related technical/humanities field.

Preferred Education:

  • Master’s degree in Information Science, Knowledge Management, Data Science, Computational Linguistics, or MBA with relevant technical focus.

Relevant Fields of Study:

  • Information Science / Library Science
  • Computer Science / Software Engineering
  • Data Science / Analytics
  • Human-Computer Interaction / UX
  • Linguistics / Computational Linguistics
  • Knowledge Management / Business Information Systems

Experience Requirements

Typical Experience Range:

  • 3–7 years of hands-on experience in enterprise knowledge management, search engineering, data engineering, content operations, or applied AI for knowledge systems.

Preferred:

  • 5+ years of direct experience designing and operating enterprise search or KM systems, or 3+ years with demonstrated experience delivering production-quality semantic search/LLM retrieval solutions and managing KM governance at scale.
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