netomi - Senior Technical PM, Applied AI
Requirements
• Roughly 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: you can prioritize, work with customers and get capabilities shipped. • Customer-first: you turn enterprise needs into clear, actionable requirements. • Excellent communicator who can align engineering, research and go-to-market teams and present clearly 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. • 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 with voice AI. • Experience in regulated or mission-critical industries (airlines, banking, healthcare, payments) and with large SI partners. • Public work you can show: a repo, demo, blog post or talk.
Responsibilities
• Agent reasoning and orchestration: multi-step task planning, action selection, retry and fallback logic, and supervisor and sub-agent patterns. • Tool and API use: reliable, observable, policy-compliant tool calling for CRM, ITSM and custom enterprise systems, including MCP. • Memory and context: conversation memory, long-running task state and continuity across channels and sessions. • Human-in-the-loop: clarification, graceful escalation to live agents, approval steps for high-risk actions and hybrid autonomy modes. • Enterprise governance: guardrails, audit trails, explainable decisions, role-based access, and safe rollout through shadow mode, staged releases and rollback. • ## Hands-on work • AI labs and new capabilities: be our product point person with AI labs and model providers. Test new models and features (reasoning, tool use, realtime voice, memory) on our use cases, decide what to build, change or skip, and give labs evidence-based feedback. • Experiments and prototypes: vibe code prototypes, build agents, and connect them to real systems through APIs and MCP tools. Run offline tests, shadow mode and canary releases with clear decision criteria, and hand engineering a working reference, not just a document. • Evaluation and quality: define the Agent Readiness Scorecard and Go/No-Go criteria. Design evals for task completion, tool success, policy compliance, latency and cost, and keep regression tests running on every model, prompt or tool change. • Debugging production failures: read agent traces with engineering, find root causes (wrong action, missed escalation, bad tool call, memory gaps) and make sure each serious failure gets a fix and a test. • Model choices: inform model selection and routing decisions by weighing quality, latency and cost with real data.
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