Senior Product Manager - AI SDLC

DisplaiMcDonald, OH
Hybrid

About The Position

Displai builds digital signage and sales gamification software. Our signage platform turns ordinary screens into managed, content-driven displays. Hoopla, our gamification product, makes sales performance visible and competitive on those same screens and beyond. We are a small, fast product organization that ships continuously and expects every person to own outcomes, not tickets. We build AI-first. That is not a slogan about features we sell. It describes how we work. Our product development runs through an agentic harness where ideas, requirements, feature specs, and implementation plans are authored as version-controlled documents, reviewed in pull requests, and shepherded by AI agents the PM drives directly. If that sounds unfamiliar, read the next section carefully, because it is the center of this job. You will own a product area end to end. Discovery, definition, specification, prototyping, triage, and the handoff into engineering all sit with you. You will work independently. The CPO sets direction and priorities and is your partner on the hard calls, but nobody will project-manage you through the day. We are hiring a senior PM because we need someone who can take an ambiguous problem and drive it to a shipped, well-specified outcome without supervision. You bring solutions, not problems. When you hit a hard call, you come with a recommendation and the reasoning behind it, not an open question for someone else to answer. You tell us what we should do. We would rather argue with a strong, well-formed point of view than fill in the blanks of an undecided one. Escalate the genuinely consequential decisions, but escalate them as proposals. There is no delivery manager or designer brokering your work. You sit directly with engineers and designers, you bring them context early rather than finished specs late, and you stay close enough to the build that nothing ships that surprises you. The defining trait of how we work is that AI is in the loop at every stage, and you are the one operating it. You will use AI to draft and pressure-test requirements, to prototype quickly, to decompose features into plans, and to keep product documentation in sync with what engineering is actually building. You should already be comfortable doing PM work this way. You should also be able to do all of it the old way, with your own judgment and your own writing, because knowing when not to trust the tool is half the skill.

Requirements

  • Several years as a product manager owning real software products end to end, at a senior or lead level.
  • A track record of shipping SaaS, not just planning it.
  • Strong product definition skills. You can take a vague problem and produce a specification that holds up under engineering scrutiny.
  • A bias toward decision. You arrive with recommendations and drive them, rather than surfacing problems and waiting for direction.
  • Demonstrated experience doing PM work AI-first: using AI tooling for definition, prototyping, and specification as a normal part of how you operate. We will ask you to show us how you work, not just describe it.
  • The judgment to do the same work without AI when that is the right move, and to catch the tool when it is wrong.
  • Comfort working in GitHub from the product side: PRs, reviews, and version-controlled documentation.
  • The ability to work independently and be trusted with an area.
  • Clear, precise writing. Most of this job is communicating intent in a form other people can act on.

Nice To Haves

  • Fluency driving an agentic, document-authoring harness like ours, where AI agents do structured product work under your direction. Few candidates will have this exact experience. If you do, it moves you to the front.
  • Experience with digital signage, sales enablement, or gamification.
  • Comfort reading code and engaging with technical tradeoffs directly.

Responsibilities

  • Own a product area from problem to shipped feature. Set the direction within it, make the calls, and answer for the result.
  • Run discovery and turn what you learn into clear, prioritized problems worth solving.
  • Write requirements and feature specifications that engineers can build from without a meeting to decode them. Do this with AI assistance and without it, depending on what the work needs.
  • Prototype with AI to make ideas concrete early, so we are arguing over something real instead of a paragraph.
  • Decompose features into implementation plans and hand them cleanly into the engineering loop.
  • Triage incoming work, bugs, and feedback. Decide what matters now, what waits, and what we will not do.
  • Work in GitHub as a first-class part of the job: pull requests, review threads, version-controlled docs, and the status of work as it moves.
  • Operate our agentic product harness day to day, or learn to fast. Drive the document-authoring workflow, keep specs and reality in sync, and use the tooling to move faster rather than fighting it.
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