What you'd actually do

Lead and develop a high-performing engineering team responsible for designing and delivering modern AI platforms and agent solutions across business workflows, fostering a culture of continuous learning and staying current with state-of-the-art advancements in AI

Define and contribute to the technical vision and long-term roadmap for foundational AI systems, owning the direction for the execution engine, including model integration, proactive agent behavior, and evaluation infrastructure

Evaluate and select AI platforms such as agentic frameworks and enterprise search solutions while remaining hands-on in developing AI agents leveraging ADK, MCP, and A2A protocols

Identify tooling gaps in enterprise AI and agentic architecture — including long-term memory, caching, agent marketplace, and agent registry — and drive solutions to ensure tools and agents perform effectively at scale

Oversee the full lifecycle — design, development, testing, deployment, and support — of AI software components, including foundation model fine-tuning, large language model inference, guardrails, model evaluation, agent evaluations, governance, and observability

Skills

Required

8+ years of experience developing AI/ML algorithms or technologies (or 6+ years with Master's)
4+ years of people management experience
Experience architecting and deploying enterprise AI agents using GenAI/LLMs and enterprise AI platforms
Strong technical foundation in software and AI
Proficiency in Python
Experience with agent development tooling (e.g., LangChain, LangGraph workflows, A2A framework)
Knowledge of AI governance, security, and compliance frameworks
Excellent communication and presentation skills

Nice to have

Experience deploying scalable and responsible AI solutions on cloud platforms (AWS, Google Cloud, Azure, Snowflake)
Familiarity with semantic model building and designing enterprise-wide context layers
Hands-on experience with LangChain, LangGraph workflows, A2A framework, or equivalent agent development tooling
Exposure to CI/CD pipelines for AI/ML model and agent deployment lifecycles
Passion for staying current with the latest AI research and judiciously applying novel techniques in production

Other signals

leading engineering team

architecting and deploying enterprise AI agents

defining technical vision and roadmap for foundational AI systems

evaluating and selecting AI platforms

identifying tooling gaps in enterprise AI and agentic architecture

overseeing full lifecycle of AI software components

establishing governance frameworks for agentic systems

ensuring robust security posture across AI platforms and agents

partnering with cross-functional teams to deliver AI-powered products

introducing state-of-the-art LLM optimization techniques

driving adoption of scalable AI tools

building dashboards and reporting mechanisms

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 seasoned Generative AI manager to join our dynamic team who is eager to solve enterprise problems with GenAI. We embarked upon many critical AI initiatives to help improve employee productivity, developer productivity, and improve business growth. You will be directly involved in innovating and contributing to these highly demanding AI initiatives. The ideal candidate will have a strong background in artificial intelligence, and machine learning, with a specific focus on agentic platforms and agent development. You will be responsible to recruit highly talented engineers, motivate and retain them for business growth. This position is a people manager role reporting to the Senior Director. Responsibility Lead and develop a high-performing engineering team responsible for designing and delivering modern AI platforms and agent solutions across business workflows, fostering a culture of continuous learning and staying current with state-of-the-art advancements in AI Define and contribute to the technical vision and long-term roadmap for foundational AI systems, owning the direction for the execution engine, including model integration, proactive agent behavior, and evaluation infrastructure Evaluate and select AI platforms such as agentic frameworks and enterprise search solutions while remaining hands-on in developing AI agents leveraging ADK, MCP, and A2A protocols Identify tooling gaps in enterprise AI and agentic architecture — including long-term memory, caching, agent marketplace, and agent registry — and drive solutions to ensure tools and agents perform effectively at scale Oversee the full lifecycle — design, development, testing, deployment, and support — of AI software components, including foundation model fine-tuning, large language model inference, guardrails, model evaluation, agent evaluations, governance, and observability Architect a scalable semantic and contextual layer, defining the processes and tooling required to establish a unified system of reference for context across all AI systems and agents Establish governance frameworks for agentic systems, including policy-as-code enforcement, human-in-the-loop approval patterns for high-risk actions, audit trails, and data handling controls (PII/PHI redaction, retention, encryption) to meet regulatory and compliance requirements Ensure robust security posture across all AI platforms and agents, including identity and access management (RBAC/ABAC), secrets management, threat modeling, and adversarial testing to safeguard enterprise systems and data Partner with cross-functional teams of engineers, data scientists, and product managers to deliver AI-powered products that accelerate agent development and deployment Introduce state-of-the-art LLM optimization techniques to improve scalability, cost efficiency, latency, and throughput of large-scale production AI systems Drive adoption of scalable AI tools across IT and business workflows, ensuring measurable ROI through structured evaluation and deployment practices Build dashboards and reporting mechanisms to track AI performance, ROI, and FinOps metrics, enabling data-driven decision-making across stakeholders 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 Bachelor's degree in Computer Science, Engineering, AI, or related field plus 8+ years of experience developing AI/ML algorithms or technologies, or a Master's degree in the same fields plus 6+ years of equivalent experience 4+ years of people management experience in a top-tier SaaS or product company, with a proven track record of building and scaling engineering teams Experience architecting and deploying enterprise AI agents using GenAI/LLMs and enterprise AI platforms Strong technical foundation with the ability to guide architectural decisions in a fast-moving, ambiguous AI product space Comfortable with ambiguity, thriving in to drive clarity from complexity Deep engineering expertise across software and AI, with the ability to identify and exploit optimization opportunities others miss Preferred Experience deploying scalable and responsible AI solutions on cloud platforms such as AWS, Google Cloud, Azure, or Snowflake Familiarity with semantic model building and designing enterprise-wide context layers Proficiency in Python with hands-on experience in LangChain, LangGraph workflows, A2A framework, or equivalent agent development tooling Knowledge of AI governance, security, and compliance frameworks in enterprise environments Exposure to CI/CD pipelines for AI/ML model and agent deployment lifecycles Passion for staying current with the latest AI research and judiciously applying novel techniques in production Excellent communication and presentation skills, with the ability to articulate complex AI concepts to both technical and non-technical audiences 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 accommodations@docusign.com. If you experience any issues, concerns, or technical difficulties during the application process please get in touch with our Talent organization at taops@docusign.com for assistance. Applicant and Candidate Privacy Notice #LI-Hybrid #LI-SA4

Senior Manager, Genai

What you'd actually do

Skills

Required

Nice to have

What the JD emphasized

Other signals