Lead AI Architect

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).

We are seeking a Lead AI Architect to turn enterprise data, metadata, relationships, and business semantics into a reusable AI-ready context foundation for intelligent assistants, autonomous agents, and future enterprise AI capabilities. You will lead the design of AI-ready data models, semantic layers, knowledge graphs, and context architectures that power: Retrieval-Augmented Generation (RAG), AI copilots and intelligent assistants, Autonomous and agentic AI workflows, Enterprise search and reasoning systems and Context-aware analytics and decision platforms. This is a strategic architecture role focused on enabling scalable, trustworthy, and business-aligned enterprise AI systems. You will partner across Enterprise Architecture, AI CoE, Data, Product, and Engineering teams to shape long-term enterprise AI architecture standards and foundational capabilities. This position is an individual contributor role reporting to the Sr. Director Data Engineering & Architecture Responsibility Define and own the enterprise AI data and context architecture across structured, semi-structured, and unstructured data Design AI-ready semantic and context layers that support LLMs, RAG systems, and AI agents Architect scalable context engineering frameworks for retrieval, grounding, memory, and reasoning Establish reusable patterns and standards for data-to-context pipelines powering enterprise AI applications Design and evolve enterprise knowledge graphs and context graphs representing business entities, relationships, metadata, and operational semantics Define ontologies, entity models, taxonomies, and semantic interoperability standards across enterprise domains Enable entity resolution, metadata harmonization, lineage-aware relationships, and contextual enrichment Drive graph-based approaches for enterprise intelligence, discovery, and semantic retrieval Partner with AI/ML, product, and engineering teams to operationalize AI copilots, autonomous agents, and intelligent assistants Design architectures supporting semantic retrieval, vector search, hybrid search, and multi-step reasoning workflows Enable trusted AI systems through high-quality contextual and semantic data foundations Contribute to enterprise AI architecture patterns, reusable templates, and governance standards Integrate enterprise platforms including CRM, SaaS, APIs, operational systems, data lakes, and warehouses into a unified knowledge and context layer Ensure scalability, governance, lineage, observability, and quality across enterprise AI knowledge systems Align AI data architectures with enterprise governance, privacy, compliance, and responsible AI standards Partner with Enterprise Architecture and AI CoE teams to align AI solutions with enterprise technology strategy Evaluate emerging AI, semantic, graph, and retrieval technologies to advance enterprise AI capabilities Influence enterprise-wide AI and data architecture direction and long-term roadmap Mentor architects and engineers on AI-ready data modeling and semantic architecture best practices Collaborate with senior leaders, architects, engineers, analysts, and vendors to evaluate and implement strategic AI solutions Job Designation Hybrid: Employee divides their time between in-office and remote work. Access to an office location is