Knowledge Engineer

Safe-Guard Products International LLC•Atlanta, GA

9h•Hybrid

About The Position

We are building a trusted, enterprise-wide knowledge foundation to support ServiceNow workflows, Microsoft 365 Copilot, and GenAI-enabled chat and decision-support platforms. As a Knowledge Engineer, you will be responsible for designing, curating, and governing high-quality knowledge assets across ServiceNow, Confluence, SharePoint, and OneDrive, ensuring content is accurate, standardized, discoverable, and reusable by both humans and AI systems. This role works cross-functionally with business teams, SMEs, IT, and AI/GenAI platform teams to transform fragmented information into structured, AI-ready knowledge that scales across the organization.

Requirements

3–5 years of experience in knowledge management, content engineering, technical documentation, or enterprise enablement roles.
Hands-on experience with Confluence, SharePoint, and OneDrive.
Strong understanding of information architecture, taxonomy, and content lifecycle management.
Experience working cross-functionally with business, IT, and operations teams.
Excellent written communication and content structuring skills.
Ability to translate complex processes into clear, standardized documentation.
Experience supporting ServiceNow knowledge management.
Exposure to GenAI, RAG, or AI-powered search/chat systems.
Familiarity with Microsoft 365 Copilot or enterprise productivity platforms.
Experience in regulated industries (insurance, auto warranty, financial services).
Understanding of metadata, tagging, and search optimization.
Knowledge of governance, compliance, and data privacy principles

Responsibilities

Design and maintain enterprise knowledge structures.
Define information architecture, taxonomy, naming conventions, and metadata standards.
Ensure knowledge is consistent, searchable, and version-controlled across platforms.
Eliminate duplication and drive reuse of authoritative content.
Establish and enforce standard templates for: Policies and procedures How-to guides and FAQs Operational playbooks Decision trees and troubleshooting guides
Normalize content into consistent formats suitable for: ServiceNow knowledge articles GenAI retrieval (RAG) M365 Copilot consumption
Implement content quality checks for accuracy, completeness, and clarity.
Manage review cycles, ownership, and content lifecycle (draft → approved → archived).
Work directly with SMEs, business leaders, and frontline teams to: Elicit tacit knowledge Convert tribal knowledge into documented standards Resolve conflicting or outdated information
Facilitate working sessions and reviews to validate content correctness.
Act as a bridge between business teams and AI/IT teams.
Prepare and structure knowledge for seamless reuse in ServiceNow: Incident, problem, and request workflows Agent assist and self-service portals
Map enterprise knowledge sources to ServiceNow knowledge models.
Ensure content supports operational resolution, not just documentation.
Partner with AI/ML and platform teams to: Prepare knowledge for GenAI-enabled chat and copilots Support Retrieval-Augmented Generation (RAG) pipelines
Apply AI-friendly practices: Clear chunking and modularization Explicit definitions and terminology Source attribution and traceability
Help define what content is authoritative vs contextual for AI usage.
Optimize SharePoint and OneDrive content for effective use by Microsoft 365 Copilot.
Ensure permissions, metadata, and content structure align with Copilot access models.
Partner with IT and business teams to identify high-value employee workflows where Copilot can surface trusted knowledge.
Support governance models for Copilot-enabled content usage.
Define ownership, access controls, and approval workflows for enterprise knowledge.
Ensure compliance with data privacy, security, and retention policies.
Maintain auditability and traceability of content used by AI systems.
Support Responsible AI practices by ensuring GenAI consumes approved, accurate sources only.
Educate teams on how to contribute, maintain, and use enterprise knowledge correctly.
Create documentation, guidelines, and best practices for content authors.
Measure adoption, reuse, and effectiveness of knowledge assets.
Continuously improve based on feedback from employees and AI system performance.