Senior Product Manager, AI

DocuSign DocuSign · Enterprise · Bangalore, India · IT Infrastructure & Operations

Product Manager for AI focusing on agent use case definition, business outcomes, and technical requirements for enterprise applications. The role involves designing agent workflows, managing collaboration and value measurement, and defining autonomous boundaries and safety mechanisms. Responsibilities include identifying high-value problems for agents, analyzing funnel metrics, influencing stakeholders, building prototypes, and partnering with engineering and ML teams to develop scalable product experiences.

What you'd actually do

  1. own Agent Use Case Definition & Business Outcomes by identifying high-impact business workflows.
  2. define the end-to-end workflow an agent will perform across enterprise systems, specifying triggers, context, actions, and escalation paths.
  3. determine the extent of agent autonomy, defining guardrails, approval checkpoints, and the human-in-the-loop model.
  4. Identify the highest-value problems for business development, sales and account executives where an agent can save time, reduce effort, and take useful action
  5. Build scrappy but convincing prototypes using available models, agent frameworks, tools, and lightweight interfaces to test usefulness, trust, and feasibility

Skills

Required

  • 6+ years of product management experience
  • meaningful work on AI/ML products, automation systems, developer tools, platforms, or other technically sophisticated products
  • Hands-on builder mindset
  • ability to use model APIs, agent tooling, and simple code in Python or TypeScript/JavaScript to create or guide working prototypes
  • Proven track record of taking new product areas from ambiguous opportunity to strategy, prototype, launch, and measurable impact
  • Strong technical fluency
  • Strong judgment in trust-sensitive experiences, including transparency, control, privacy, and reliability
  • Excellent written and verbal communication skills
  • Strong Data Analysis, Data Science and engineering skills

Nice to have

  • building assistants, agents, copilots, or AI products that use tools to complete tasks
  • Familiarity with memory, tool calling, browser or computer use, voice, multimodal interaction, or agent orchestration
  • Background in software engineering, ML, data science, or a similarly technical field
  • Strong point of view on when an agent should act autonomously, when it should ask for permission, and how to keep users in control

What the JD emphasized

  • AI/ML products
  • automation systems
  • agent frameworks
  • prototypes
  • memory, context, orchestration, permissions, and human handoff
  • agent autonomy
  • guardrails
  • human-in-the-loop
  • trust, reliability, latency, and cost

Other signals

  • AI/ML products
  • automation systems
  • agent use case definition
  • agent capabilities
  • workflow design and orchestration
  • autonomous boundaries and safety
  • agent frameworks
  • prototypes
  • memory, context, orchestration, permissions, and human handoff