Senior Product Manager - Autonomous Agents

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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Mental health support

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Relocation assistance

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Market context

U.S. role benchmark (BLS OEWS)

$102,662 U.S. median for this role

Projected growth (BLS Employment Projections)

+5.4% - Faster than average

Matched to SOC 11-1021 - Product Management aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Product From the posting source checked Jun 20, 2026

Seniority

Senior From the posting source checked Jun 20, 2026

Schedule

Shift type

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Weekend work

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Application

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Assessment

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Deadline

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Where they hire

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About this role

Senior Product Manager - Autonomous Agents Gurugram About the Company: Netomi is the leading agentic AI platform for enterprise customer experience. We work with the largest global brands like Delta Airlines, MetLife, MGM, United, and others to enable agentic automation at scale across the entire customer journey. Our no-code platform delivers the fastest time to market, lowest total cost of ownership, and simple, scalable management of AI agents for any CX use case. Backed by WndrCo, Y Combinator, and Index Ventures, we help enterprises drive efficiency, lower costs, and deliver higher quality customer experiences. Want to be part of the AI revolution and transform how the world's largest global brands do business? Join us! About the Role Netomi is hiring a Senior Product Manager to own the product strategy and execution roadmap for Autonomous Agents in the conversational AI domain. This role is responsible for defining how Netomi's agents reason, plan, execute multi-step tasks, and collaborate across channels-from chat and voice to email and messaging-in production-grade enterprise environments. You will work at the intersection of agentic AI research, real-world enterprise deployments, and platform reliability. You will define what autonomous agents look like for Netomi customers: how they understand goals, invoke tools and APIs, manage state across long-horizon tasks, recover from failures, and hand off gracefully to human agents when needed. You should understand what 'successful autonomous agent deployment' looks like in production: multi-turn reasoning loops, tool/workflow execution reliability, memory and context management, human-in-the-loop escalation, observability of agent behavior, and cost/latency

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