AI Product Manager
T
TribalScaleAI product consulting
Location: Remote CanadaFull-TimeMiddle
Salary not disclosed
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Job Details
- Experience
- 5+ years in product management
- Required Skills
- Product Management
Requirements
- Have 5+ years of product management experience with ownership of scope, roadmap, and delivery outcomes.
- Bring agency or consulting experience managing multiple clients or accounts.
- Have run at least two of the following: a zero-to-one greenfield build, a legacy platform rebuild or migration, and a client-facing enablement or training engagement.
- Have hands-on experience scoping, building, or shipping AI agents or agentic workflows, beyond prompting a chatbot.
- Understand tradeoffs between agentic and deterministic approaches.
- Be able to discuss architecture, evaluations, and guardrails with agentic engineers.
- Be able to explain agentic concepts to non-technical stakeholders and engineering teams.
- Be comfortable presenting to VP-level stakeholders, handling pushback, and declining out-of-scope requests.
- Be able to turn vague briefs into concise plans and defend prioritization tradeoffs.
- Be comfortable starting work amid ambiguity without a fully formed specification.
- Nice to have: experience with LLM evaluation frameworks or agent observability tooling.
- Nice to have: a technical background in engineering, data, or QA.
Responsibilities
- Own client engagements from discovery and scoping through delivery, serving as the primary product voice with clients.
- Turn zero-to-one ideas into sequenced, shippable roadmaps for greenfield product builds.
- Lead legacy platform rebuild and modernization engagements while balancing technical debt and delivery speed.
- Work with client product and engineering teams to build their capabilities in agentic development.
- Translate ambiguous client requests into scoped work by writing PRDs, defining success criteria, and sequencing delivery.
- Partner with agentic engineers to choose between deterministic workflows and agentic use cases.
- Set guardrails and evaluation criteria for AI agents before they access client data or production environments.
- Manage multiple concurrent engagements and competing client timelines, budgets, and priorities.
- Lead client steering committees, sprint reviews, and discussions when scope, timelines, or feasibility change.
- Define and report engagement outcomes and ROI, and contribute to internal delivery playbooks.
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