Electronic Arts

Knowledge Manager – AI Systems
18 Month Temporary Contract

We are seeking a Knowledge Management Lead to define, operate, and scale the knowledge management capabilities that power AI-driven player support and fan experiences.

This role sits at the intersection of product, content, operations, and technology. You will partner with Product, Engineering, Content, Fan Care, and Studio teams to ensure knowledge is structured, governed, accurate, and optimized for both human and machine consumption.

The role is responsible for treating knowledge as a core dependency for AI systems: machine-readable, versioned, traceable, reusable, and safe for real-time retrieval and execution. You will help define the strategy while also operating the workflows, standards, and governance needed to manage knowledge at scale.

This role reports to the Director of Product for Self-Service Fan Care.

Key responsibilities

1. Knowledge strategy and structure

• Define and evolve the knowledge management strategy for AI-driven player support.

• Translate product and AI needs into knowledge models, schemas, metadata, taxonomies, and system requirements.

• Define knowledge structure, metadata, taxonomy, and governance requirements that enable reliable search, retrieval, API access, and AI consumption.

• Ensure knowledge is modular, reusable, and structured for both human and machine consumption.

2. Knowledge operations and lifecycle management

• Own the knowledge lifecycle: intake, creation, validation, publishing, versioning, monitoring, and deprecation.

• Operate and improve workflows for knowledge updates, quality control, and real-time change management.

• Identify knowledge gaps, duplication, conflicts, and stale content, then drive resolution with owning teams.

• Drive the creation and curation of accurate, structured, AI-ready knowledge from priority I've shared source materials and player signals in partnership with other teams.

3. AI readiness, retrieval, and optimization

• Partner with Product and Engineering to improve how AI systems retrieve, assemble, and use knowledge.

• Diagnose AI output issues by tracing failures back to gaps in knowledge structure, quality, coverage, or governance.

• Improve retrieval performance, including relevance, precision, recall, freshness, and latency.

• Contribute to evaluation frameworks that connect knowledge quality to AI performance and player outcomes.

4. Governance, quality, and risk controls

• Establish governance models for ownership, approvals, access controls, certification, and escalation.

• Implement standards for versioning, traceability, auditability, freshness, and source authority.

• Define controls that reduce risk from outdated, conflicting, unapproved, or misapplied knowledge.

• Ensure policy, instructional, and factual knowledge are clearly separated and enforceable for AI systems.

5. Cross-functional enablement

• Drive cross-functional adoption of AI-ready knowledge standards, including structured authoring, tagging, validation, ownership, and lifecycle governance.

• Enable knowledge contributors to create, curate, and maintain reusable knowledge that improves AI accuracy, trust, and player experience.

Required qualifications

• 4–6+ years in Knowledge Management, Content Systems, Content Operations, Information Architecture, or related roles.

• Experience operating enterprise knowledge, CMS, or content systems in digital product environments.

• Strong understanding of metadata, taxonomy, ontology, structured content, and information architecture.

• Experience with knowledge lifecycle management, governance workflows, and content quality standards.

• Understanding of search, retrieval, RAG, or AI knowledge consumption patterns.

• Strong analytical and systems-thinking skills, especially in diagnosing how information flows through digital systems.

• Experience working cross-functionally with Product, Engineering, Content, Operations, and business stakeholders.

Preferred qualifications

• Experience supporting AI, conversational, agentic, or automation-based systems.

• Familiarity with RAG, GraphRAG, vector search, knowledge graphs, semantic layers, or API-based knowledge access.

• Experience with traceability, auditability, versioning, and governance for AI or regulated systems.

• Experience scaling knowledge practices across multiple teams, regions, products, or content sources.

• Experience working with multimodal knowledge, including structured data, text, media, and community-generated signals.