Product Builder (product Manager), AI Agents

Apollo.io Apollo.io · Enterprise · United States · Product

Product Manager role focused on building and scaling AI Agents for a go-to-market platform. The role involves defining agentic orchestration, progressive autonomy frameworks, multi-channel delivery, and AI quality/evaluation infrastructure. Requires hands-on prototyping, customer engagement, and driving adoption of autonomous AI products.

What you'd actually do

  1. Agentic orchestration spanning scheduled, event-driven, and user-initiated execution modes, defining how the assistant continuously works on behalf of each rep across heartbeat routines, real-time signal responses, and direct requests.
  2. Progressive autonomy framework that governs how the assistant earns trust over time, moving from fully supervised actions to autonomous execution within admin-defined boundaries.
  3. Multi-channel delivery so the assistant works where reps work: Slack, iMessage, WhatsApp, email, SMS, and in-product, with daily briefs, hot lead alerts, draft outreach, meeting prep, and escalations.
  4. AI quality and evaluation infrastructure across accuracy, relevance, and reliability. Nothing ships without hitting quality thresholds.
  5. Go deep with customers. Understand their workflows through direct engagement and research, at the level of someone who has lived them. Use that understanding to set strategy and own your roadmap.

Skills

Required

  • AI-native builder
  • prototype with tools like Claude Code and Cursor
  • High agency and strong ownership
  • Product judgment
  • Customer empathy
  • Execution excellence
  • Technical fluency
  • Domain expertise
  • Strong collaborator
  • Experience shipping agentic AI products
  • Deep understanding of LLMs, RAG pipelines, agent frameworks, orchestration, trust models, failure handling, and evaluation of autonomous systems at scale

Nice to have

  • built your own GTM agent

What the JD emphasized

  • Experience shipping agentic AI products
  • put autonomous agents into production
  • handled real workflows and drove measurable business outcomes
  • Deep understanding of LLMs, RAG pipelines, agent frameworks, orchestration, trust models, failure handling, and evaluation of autonomous systems at scale

Other signals

  • AI Agents
  • Autonomous Agents
  • Progressive Autonomy
  • Agentic Orchestration
  • Evaluation Infrastructure