Lead AI Architect

Salary

$165k – $266k

Location

US-SFO-Main, United States

Work mode

hybrid

Type

full-time

Department

IT Infrastructure & Operations

Equity

Included

Last seen

23h ago

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). What you’ll do 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 required. (Frequency: Minimum 2 days per week; may vary by team but will be weekly in-office expectation) Positions at Docusign are assigned a job designation of either In Office, Hybrid or Remote and are specific to the role/job. Preferred job designations are not guaranteed when changing positions within Docusign. Docusign reserves the right to change a position’s job designation depending on business needs and as permitted by local law. What you bring Basic 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 Experience with 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 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 Wage Transparency 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 below details pay ranges in the following locations: California: $164,700.00 - $266,000.00 base salary This role is also eligible for the following: Bonus: Sales personnel are eligible for variable incentive pay dependent on their achievement of pre-established sales goals. Non-Sales roles are eligible for a company bonus plan, which is calculated as a percentage of eligible wages and dependent on company performance. Stock: This role is eligible to receive Restricted Stock Units (RSUs). Global benefits provide options for the following: Paid Time Off: earned time off, as well as paid company holidays based on region Paid Parental Leave: take 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 Life at Docusign Working here Docusign is committed to building trust and making the world more agreeable for our employees, customers and the communities in which we live and work. You can count on us to listen, be honest, and try our best to do what’s right, every day. At Docusign, everything is equal. We each have a responsibility to ensure every team member has an equal opportunity to succeed, to be heard, to exchange ideas openly, to build lasting relationships, and to do the work of their life. Best of all, you will be able to feel deep pride in the work you do, because your contribution helps us make the world better than we found it. And for that, you’ll be loved by us, our customers, and the world in which we live. Accommodation Docusign is committed to providing reasonable accommodations for qualified individuals with disabilities in our job application procedures. If you need such an accommodation, or a religious accommodation, during the application process, please contact us at [email protected]. If you experience any issues, concerns, or technical difficulties during the application process please get in touch with our Talent organization at [email protected] for assistance. Applicant and Candidate Privacy Notice States Not Eligible for Employment This position is not eligible for employment in the following states: Alaska, Hawaii, Maine, Mississippi, North Dakota, South Dakota, Vermont, West Virginia and Wyoming. Equal Opportunity Employer It’s important to us that we build a talented team that is as diverse as our customers and where all employees feel a deep sense of belonging and thrive. We encourage great talent who bring a range of perspectives to apply for our open positions. Docusign is an Equal Opportunity Employer and makes hiring decisions based on experience, skill, aptitude and a can-do approach. We will not discriminate based on race, ethnicity, color, age, sex, religion, national origin, ancestry, pregnancy, sexual orientation, gender identity, gender expression, genetic information, physical or mental disability, registered domestic partner status, caregiver status, marital status, veteran or military status, or any other legally protected category. EEO Know Your Rights poster #LI-Hybrid

Basic 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 Experience with 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 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

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

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