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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Lead Product Manager, Agentic Quality Engineering Hub - **Company:** FRACTAL LLC - **Location:** Medina, WA, United States - **Experience:** Expert - **Salary:** $125,000.0 - $185,000.0 - **Contract:** Permanent contract - **Skills:** Testing (Software), Application Programming Interfaces (APIs), Artificial Intelligence, Android Software Development, Apple IOS, JIRA, Automation of Tests, Unit Testing, Microsoft Azure, Bug Tracking Systems, Software Bug Management, Code Coverage, Continuous Integration, Database Testing, DevOps, Github, Information Retrieval, Regression Testing, E2e Testing, Responsive Web Design, Service Virtualization, Smoke Testing, SoapUI, Test Data, Test Management, Strategies of Testing, Web Testing, Software Repository, Postman, Large Language Models, Prompt Engineering, Agentic-AI, AI Platforms, Gitlab-ci, Playwright, Graphql, Browser Testing, Api Management, Human in the Loop, Testrail, Jenkins - **Published:** October 2, 2026 - **Apply:** https://www.disabledperson.com/jobs/75686594-lead-product-manager-agentic-quality-engineering-hub ## About the Role * 10+ years of experience in Quality Assurance, with demonstrated depth across test strategy, execution, automation, defect management, and quality governance. * Subject Matter expert of Quality Assurance and Quality Engineering practices, including test strategy, automation, regression, exploratory testing, defect management, CI/CD, and quality metrics. * Strong product ownership or product management experience, preferably with platforms, internal tools, developer experience, QE, automation, or enterprise AI. * Ability to translate ambiguous user needs into clear product outcomes, prioritized backlogs, and adoption plans. * Experience working with cross-functional technology teams, including engineering, architecture, security, infrastructure, and business stakeholders. * Comfort evaluating technical solutions, proofs of concept, and team-built accelerators for enterprise reuse. * Strong communication, facilitation, stakeholder management, and evangelism skills. * Familiarity with AI agents, workflow automation, prompt patterns, MCP/tool integrations, governance, or responsible AI practices is strongly preferred. Technical Skills: * Quality Engineering tools and platforms: Experience with test management, planning, automation and defect tracking tools such as Jira, TestRail, Playwright, or similar web and mobile testing frameworks. * API and service testing: Familiarity with API testing concepts and tools such as Postman, REST Assured, SoapUI, GraphQL testing, contract testing, and service virtualization. * Testing practices and methodologies: Understanding of unit testing, functional testing, data testing, web testing, mobile testing, integration/API testing, end-to-end testing, regression testing, smoke testing, and acceptance testing. * Web and mobile quality practices: Experience supporting quality practices for web, iOS, Android, responsive design, cross-browser testing, accessibility testing, and mobile device coverage. * CI/CD and DevOps integration: Understanding of how automated tests integrate into CI/CD pipelines using tools such as GitHub Actions, Jenkins, GitLab CI, Azure DevOps, or similar platforms. * AI and agentic tooling: Familiarity with AI-assisted testing, agent workflows, prompt engineering, MCP/tool integrations, LLM-based automation, human-in-the-loop controls, and responsible AI practices. * Observability and quality insights: Working knowledge of logs, monitoring, telemetry, dashboards, defect trends, test coverage, flaky test analysis, and production quality signals. ## Description The Product Owner for the Agentic QE Hub owns the vision, roadmap, adoption, and measurable impact of our premier retail client's agentic Quality Engineering capabilities. This role is responsible for shaping the tools, processes, standards, and enablement model that help QE teams use AI agents safely and effectively across test strategy, test design, automation, defect analysis, quality insights, reporting, and engineering workflow integration., * Define and own the vision, strategy, roadmap, and backlog for the Agentic QE Hub, aligned to enterprise AI strategy, QE transformation goals, and technology priorities. * Own the end-to-end product lifecycle for agentic QE capabilities, including research, discovery, prioritization, release planning, adoption, feedback, and continuous improvement. * Define and track product success metrics including adoption, active usage, team onboarding, cycle-time improvement, quality coverage, automation efficiency, defect detection, user satisfaction, reuse, and reduction in manual QE effort. * Understand stakeholder needs and translate them into product outcomes by working with QE leaders, quality engineers, delivery teams, product teams, engineering teams, and platform stakeholders. * Evangelize and drive adoption of the Agentic QE Hub by communicating the vision, sharing success stories, leading demos, promoting reusable practices, gathering feedback, and helping teams adopt the platform and processes. * Assess agentic QE tools, agents, workflows, accelerators, and proofs of concept created by other teams and the industry (for example Agentic Testing, no code/low code options, RPA, automation etc); determine what should be adopted, improved, integrated, scaled, governed, or retired. * Drive the evolution of core Agentic QE Hub capabilities, including agent workflows, reusable prompts, test generation patterns, MCP/tool integrations, reporting, knowledge retrieval, governance, and human-in-the-loop controls. * Partner across Engineering, Architecture, Security, Legal, Infrastructure, AI platform, and business teams to deliver secure, reliable, integrated, and enterprise-ready QE solutions. * Define standards and operating processes for agentic QE capabilities, including how they are evaluated, onboarded, documented, supported, governed, and measured across the enterprise. * Ensure Agentic QE Hub capabilities integrate with overall delivery ecosystem, including test management tools, CI/CD pipelines, code repositories, defect management, observability, documentation, and enterprise AI platforms. Success Measures: * Increased adoption of Agentic QE Hub capabilities across priority QE teams. * Reduced manual effort in test design, test data preparation, automation creation, defect analysis, or reporting. * Improved test coverage, defect detection, quality visibility, or release confidence. * Number of reusable agentic QE patterns, workflows, and tools scaled beyond a single team. * Improved satisfaction from QE users and delivery teams. * Clear governance and evaluation process for new agentic QE use cases. * Reduced duplication of AI/QE tooling across teams. * Faster onboarding time for teams adopting agentic QE practices.