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Netomi is the leading agentic AI platform for enterprise customer experience, working with global brands like Delta Airlines, MetLife, and United. The company is backed by WndrCo, Y Combinator, and Index Ventures.
You will own the product strategy and roadmap for Netomi's Agents platform, deciding what to build and why, tying every investment to enterprise customer and business outcomes, and ensuring capabilities are shipped and adopted by the world's largest brands.
This is a hands-on role combining strategic product leadership with direct technical work. You will partner with AI labs to understand new model capabilities early, build experiments and prototypes yourself, design evaluations, and debug failing agents in production.
**Product and Business Ownership:**
You will own the multi-quarter roadmap for the Agents platform end-to-end, from problem discovery through delivery and outcome tracking. You'll tie roadmap decisions to measurable business outcomes including autonomous resolution rate, cost per resolution, CSAT, revenue expansion, and competitive positioning. You'll work directly with enterprise customers, Customer Success, Delivery, and Sales to identify high-value problems and translate them into clear problem statements, PRDs, and success metrics. You'll drive cross-functional alignment across engineering, research, data science, QA, and go-to-market teams without direct authority, run quarterly planning cadences and monthly scope locks, own launch checklists and adoption tracking, and conduct post-incident reviews to feed learnings back into the roadmap.
**Product Areas:**
You will own agent reasoning and orchestration (multi-step task planning, action selection, retry/fallback logic, supervisor and sub-agent patterns); tool and API use (reliable, observable, policy-compliant tool calling for CRM, ITSM, and custom systems including MCP); memory and context (conversation memory, long-running task state, channel continuity); human-in-the-loop (clarification, escalation, approval steps, hybrid autonomy); and enterprise governance (guardrails, audit trails, explainable decisions, role-based access, safe rollout).
**Hands-On Technical Work:**
You will serve as the product point person with AI labs and model providers, testing new models and features on real use cases and providing evidence-based feedback. You'll build prototypes and agents, connect them to real systems via APIs and MCP tools, run offline tests and canary releases with clear decision criteria, and hand engineering working references. You'll define the Agent Readiness Scorecard and Go/No-Go criteria, design evaluations for task completion, tool success, policy compliance, latency and cost, and maintain regression tests. You'll debug production failures by reading agent traces, finding root causes, and ensuring fixes and tests. You'll inform model selection and routing decisions using real data on quality, latency, and cost.
**Requirements:**
- 4–8 years of relevant experience, ideally from a deeply technical product role, applied AI engineering, or research engineering with clear product ownership
- Proven ownership of a complex technical product area with features delivered to enterprise customers and measurable business results
- Strong product judgment: ability to prioritize, work with customers, and get capabilities shipped
- Customer-first mindset: translate enterprise needs into clear, actionable requirements
- Excellent communication skills to align engineering, research, and go-to-market teams and present to executives
- Comfortable vibe coding, building agents, connecting APIs and MCP tools, designing evaluations, and debugging production failures
- Deep understanding of LLM reasoning, tool calling, multi-step task execution, orchestration patterns, evaluation methods, and production observability for agents
- Technical fluency with APIs, cloud environments, and AI deployment workflows
**Nice to Have:**
- Experience shipping AI agent products at a conversational AI or enterprise AI platform company
- Direct experience working with AI labs or model providers (early-access programs, model evaluations)
- Familiarity with agent frameworks and protocols (e.g., LangGraph, MCP) and voice AI
- Experience in regulated or mission-critical industries (airlines, banking, healthcare, payments) and with large SI partners
- Public work to show: repo, demo, blog post, or talk
