Lead Information Architect, Enterprise Ontology and AI

Company Overview

Docusign brings agreements to life. Over 1.5 million customers and more than a billion people in over 180 countries use Docusign solutions to accelerate the process of doing business and simplify people’s lives. With intelligent agreement management, Docusign unleashes business-critical data that is trapped inside of documents. Until now, these were disconnected from business systems of record, costing businesses time, money, and opportunity.

Using Docusign’s Intelligent Agreement Management platform, companies can create, commit, and manage agreements with solutions created by the #1 company in e‑signature and contract lifecycle management (CLM).

A Lead Information Architect, Enterprise Ontology and AI is a visionary strategist responsible for the structural foundation of Docusign’s intelligent agreement ecosystem. You design the semantic models and knowledge frameworks that transform disconnected data into machine‑understandable intelligence, enabling our AI engines to deliver precise, context‑aware outcomes. You are the architect of the connective tissue that ensures our AI agents and search systems understand the complex relationships between documents, data, and business workflows.

A Lead Information Architect, Enterprise Ontology and AI helps drive the evolution of our global information standards by bridging the gap between human intent and technical execution. This position requires a rare blend of deep linguistic expertise, technical systems thinking, and the ability to influence high‑level technical roadmaps. You will collaborate on high‑stakes initiatives across Product, GTM, and AI Engineering to ensure a unified semantic layer that powers our next generation of Agentic AI.

This position is an individual contributor role reporting to the Director, Knowledge Management.

• Define, own, and evolve the enterprise ontology, taxonomy, and semantic layer
• Translate business intent into a unified information architecture, including canonical entities, attributes, relationships, and scalable classification systems that power the underlying intelligence of our Agentic AI solutions
• Design and maintain knowledge graph relationships that capture how agreements, parties, processes, and data connect across the business
• Implement taxonomy, ontology, and metadata frameworks in priority systems and workflows to improve retrieval accuracy, consistency, explainability, and overall Agentic AI outcomes
• Define and maintain enterprise taxonomies, controlled vocabularies, and content boundaries to ensure consistent labeling and discoverability across products, data, and content
• Build and maintain a governed business glossary with canonical definitions, synonyms, and reference rules used across Product, GTM, AI, and Analytics
• Configure and administer semantic platforms such as Semaphore or Pool Party, focusing on modeling and governance of taxonomies, ontologies, and semantic metadata
• Ensure semantic assets are exposed in machine‑usable formats to support AI, semantic search, analytics, and self‑service reporting use cases
• Establish and scale semantic standards across distributed teams, leveraging influence, credibility, and clear strategic communication to ensure company‑wide governance
• Partner with analytics, reporting, data platform, product, and AI teams to reconcile definitions and drive convergence on shared semantic models
• Create and communicate a multi‑year semantic architecture roadmap aligned with Docusign’s AI, data, and product strategies
• Establish governance processes for semantic assets, including documentation, lineage, versioning, and change‑management workflows
• Define and run review and certification processes for new or updated terms, concepts, and relationships to maintain a trusted, auditable semantic layer
• Create clear contribution and stewardship models so distributed teams know how to request changes and adopt standards at scale
• Measure the impact of information architecture on Agentic AI and search performance, refining knowledge models based on real‑world usage and feedback
• Develop and track key health and adoption metrics for enterprise ontology,…