Lead AI Architect

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, connecting it to business systems of record to save time, money, and opportunity. Using Docusign’s Intelligent Agreement Management platform, companies can create, commit, and manage agreements with solutions from the #1 company in e-signature and contract lifecycle management (CLM).

About the role

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
• 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 of Data Engineering & Architecture.

Responsibilities

• 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

Requirements

• 12+ years of experience in data architecture, data engineering, distributed data systems, or AI/data platform engineering
• Experience designing enterprise-scale data models, semantic layers, and integration architectures
• Understanding of entity modeling, relationship modeling, ontology design, and semantic interoperability
• Experience with knowledge graph technologies such as Neo4j, RDF/SPARQL, or equivalent graph platforms
• Experience integrating heterogeneous enterprise data sources including CRM, SaaS, APIs, operational platforms, and data lakes
• Programming skills in Python and SQL
• Experience with modern cloud data platforms and lakehouse architectures such as Snowflake, Databricks, or equivalent technologies
• Experience designing or implementing LLM-powered systems including RAG, semantic retrieval, grounding, and context-aware AI workflows
• Understanding of context engineering and how AI systems consume enterprise knowledge
• Experience with vector databases, embeddings, semantic search, and hybrid retrieval architectures
• Familiarity with AI orchestration and agent frameworks such as LangChain, LangGraph, Semantic Kernel, CrewAI, or equivalent ecosystems
• Experience building enterprise knowledge systems supporting reasoning, discovery, and AI-assisted decision-making

Preferred Qualifications

• Experience leading enterprise-scale AI architecture or data modernization initiatives
• Strong understanding of metadata management, lineage, governance, and Master Data Management (MDM)
• Experience with distributed and event-driven architectures including Kafka or streaming ecosystems
• Experience with cloud-native AI and data services across AWS, Azure, or GCP
• Familiarity with enterprise AI search and knowledge platforms such as Glean or similar technologies
• Understanding of privacy, governance, and responsible AI practices for enterprise environments

Schedule

Hybrid: Employee divides their time between in-office and remote work. Access to an office location is required. Minimum 2 days per week in-office; frequency may vary by team but will include a weekly in-office expectation.

Pay

Pay for this position is based on a number of factors including geographic location and may vary depending on job-related knowledge, skills, and experience. Based on applicable legislation, the pay range in California is $164,700.00 - $266,000.00 base salary.

This role is also eligible for the following bonuses:

• Sales personnel: variable incentive pay dependent on achievement of pre-established sales goals
• Non-Sales roles: company bonus plan calculated as a percentage of eligible wages and dependent on company performance

This role is eligible to receive Restricted Stock Units (RSUs).

Benefits

• Paid time off, including earned time off and paid company holidays based on region
• Paid parental leave: up to six months off with your child after birth, adoption, or foster care placement
• Full health benefits plans: options for 100% employer-paid and minimum employee-contribution health plans from day one of employment
• Retirement plans: select retirement and pension programs with potential for employer contributions
• Learning and development: options for coaching, online courses, and education reimbursements
• Compassionate care leave: paid time off following the loss of a loved one and other life-changing events