Senior Technical PM, Applied AI

NetomiEnterprise AI

Remote - IndiaFull-TimeSenior

Salary not disclosed

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Job Details

Have roughly 4–8 years of relevant experience, ideally in a deeply technical product role, applied AI engineering, or research engineering with clear product ownership.
Have owned a complex technical product area, delivering features to enterprise customers with measurable business results.
Demonstrate strong product judgment, including prioritization, customer engagement, and shipping capabilities.
Translate enterprise customer needs into clear, actionable requirements.
Align engineering, research, and go-to-market teams and present clearly to executives.
Be comfortable building agents, connecting APIs and MCP tools, designing evaluations, and debugging production failures.
Have a deep understanding of LLM reasoning, tool calling, multi-step task execution, orchestration patterns, evaluation methods, and production observability for agents.
Have technical fluency with APIs, cloud environments, and AI deployment workflows.
Experience shipping AI agent products at a conversational AI or enterprise AI platform company is a plus.
Experience working with AI labs or model providers, agent frameworks and protocols such as LangGraph or MCP, voice AI, or regulated and mission-critical industries is a plus.

Own the multi-quarter roadmap for the Agents platform, from problem discovery and prioritization through delivery and outcome tracking.
Tie roadmap investments to measurable business outcomes, including autonomous resolution rate, cost per resolution, CSAT, revenue expansion, and competitive wins.
Work with enterprise customers and Customer Success, Delivery, and Sales to identify high-value problems and translate them into product requirements and success metrics.
Align engineering, research, data science, QA, and go-to-market teams on scope, dependencies, execution, and launch readiness.
Run quarterly roadmap inputs, monthly scope lock, weekly execution reviews, and launch readiness sign-offs.
Build agent prototypes and connect them to enterprise systems through APIs and MCP tools; run offline tests, shadow mode, and canary releases.
Design agent evaluations and readiness criteria covering task completion, tool success, policy compliance, latency, and cost.
Debug production failures with engineering, identify root causes, and ensure serious failures receive fixes and regression tests.
Own launch checklists, track adoption by capability, and publish monthly Agent Outcomes readouts.
Monitor releases, run post-incident reviews, and feed learnings back into the roadmap.

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