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Open nowPosted 31 hours ago

Senior Product Manager, Agentic AI

joynrh29 open roles

Where
Uberlândia, Minas Gerais, BR, Remote
Work mode
Remote
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Your applicationOpen nowSenior Product Manager, Agentic AIjoynrh · Uberlândia, Minas Gerais, BR, Remote

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The clock on this job

Early applications get read.

12.4% of postings close within 7 days. Measured by our own scanner across the market. joynrh postings stay open a median of 3 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.4%7 days
  4. 20.0%14 days
  5. 35.5%30 days
  6. 58.6%60 days
This job: posted 31 hours ago

joynrh median: 3 days open

The posting

The Role This senior individual-contributor role owns a defined area of our enterprise agentic AI product. You will identify valuable work for AI agents, shape how those agents are built and used, and improve their performance after launch. The job requires product judgment, technical fluency, and close work with Design, Engineering, AI/ML, Security, and customer-facing teams. You will connect agent capabilities to customer outcomes across industries and across our US and Brazilian teams.

The work moves through three modes, repeatedly: -Find valuable agent work. Study the tasks, decisions, and handoffs customers want to improve. Define the user, baseline, expected outcome, and limits of automation before proposing an agent. -Design agents people can trust. Shape tool use, data access, workflow steps, approvals, and failure behavior so agents are useful in real enterprise settings. -Improve agents in production. Use evaluations, telemetry, user feedback, and business results to improve task success, reliability, latency, and cost.

What You’ll Do ● Own the strategy, roadmap, backlog, and measurable outcomes for a defined set of agentic AI capabilities and customer workflows. ● Lead discovery with domain experts, administrators, and developers. Turn real workflows into clear problem statements, product requirements, and acceptance criteria. ● Define product behavior for agent planning, tool calling, context and memory, workflow state, human approvals, exception handling, and safe handoff to people. ● Work with Design and Engineering on usable agent-building and management experiences, including no-code configuration and developer extension points. ● Set the product quality bar for agent evaluation, testing, tracing, observability, versioning, rollback, and ongoing performance review. ● Partner with AI/ML and platform teams on model choice, retrieval, integrations, Model Context Protocol, private inference, and deployment constraints without treating infrastructure as the product goal. ● Build enterprise requirements into the experience, including permissions, data privacy, audit trails, guardrails, and policy-based controls. ● Prioritize with evidence from task success, adoption, escalation rates, quality, reliability, latency, cost, and documented customer outcomes.

● An experienced product owner. You have shipped complex software, made hard tradeoffs, and carried a product area from discovery through release and improvement. ● Agentic-AI fluent. You understand LLMs, retrieval, prompts, tool use, orchestration, context, evaluation, and where agents fail. ● Technically credible. You can work through APIs, integrations, MCP, data flows, security boundaries, and deployment options with engineers. ● Close to users. You can learn a customer's industry quickly, separate a genuine workflow problem from a feature request, and test whether an agent improves the work. ● Rigorous about quality. You define success before launch, examine failures after launch, and balance capability with reliability, cost, and human control. ● Bilingual and cross-cultural. Full professional fluency in English and Portuguese is required, along with the ability to work effectively across US and Brazilian teams and customers.

Nice to Have ● Experience shipping enterprise AI agents, workflow automation, AI assistants, developer tools, or low-code products. ● Hands-on experience with agent frameworks, RAG, evaluation methods, model routing, tool calling, or MCP integrations. ● Experience with private or multi-cloud deployments, regulated customers, SOC 2-aligned controls, or enterprise AI governance. ● Experience connecting product telemetry to business outcomes such as time saved, cycle time, error reduction, adoption, or customer retention.

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