Product Manager (agents)

Lovable Lovable · Coding AI · Stockholm, Sweden · Product

Product Manager to lead an AI agent product end-to-end, owning quality, roadmap, and feedback loops. Requires hands-on experience with agent-based systems, tool use, planning, multi-step reasoning, and shipping AI/LLM-powered products. Responsibilities include user representation, discovery, owning agent output quality, driving eval infrastructure, and defining improvements for error recovery and communication.

What you'd actually do

  1. Represent the user - synthesize findings on agent performance and behavior, and bring them to the team with clarity and conviction
  2. Run discovery end-to-end: user interviews, competitive research, eval analysis, prompt experimentation, and crafting messaging for upcoming agent capabilities
  3. Own the quality bar for agent outputs - drive eval infrastructure, monitor regressions, and ensure the agent improves with every release
  4. Scope ruthlessly - ship the right slice of functionality, validate what works through user feedback and metrics, and cut what doesn't
  5. Enable sales, support, and marketing with the context they need to communicate new agent capabilities effectively

Skills

Required

  • 6+ years in the software industry
  • direct ownership of an AI or LLM-powered product in a product management or engineering leadership role
  • Hands-on experience building and shipping agent-based systems
  • Technical fluency: program or have programmed
  • Strong product sense and systems thinking

Nice to have

  • Deep familiarity with prompt engineering
  • Deep familiarity with evaluation frameworks
  • prior experience operating at the frontier where best practices are still being written

What the JD emphasized

  • direct ownership of an AI or LLM-powered product
  • Hands-on experience building and shipping agent-based systems
  • program or have programmed
  • read model outputs, traces, and evals with confidence
  • Own the quality bar for agent outputs
  • drive eval infrastructure
  • monitor regressions
  • Rebuilding the agent evaluation framework
  • running discovery on the biggest gaps in agent reliability and trust
  • defining and shipping the first iteration of improved agent error recovery and communication

Other signals

  • leading agent end-to-end
  • owning quality, roadmap, and feedback loops
  • shaping how agent reasons, acts, and delivers
  • hands-on experience building and shipping agent-based systems
  • technical fluency to read model outputs, traces, and evals
  • owning the quality bar for agent outputs
  • driving eval infrastructure
  • monitoring regressions
  • rebuilding the agent evaluation framework
  • defining and shipping improved agent error recovery and communication