Senior QA Engineer - AI Native and Agentic Systems
New
Remote-first work environment for candidates based in India., Must have overlap during US Pacific working hours.Full-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 3+ years
- Required Skills
- CypressQA AutomationAPI testingSeleniumCI/CDPlaywright
Requirements
- 3+ years of experience in QA, including both manual testing and automation.
- Strong understanding of software development lifecycle processes, testing strategies, and complex system failure modes.
- Hands-on experience with UI automation tools such as Playwright, Cypress, or Selenium.
- Experience with API testing and validating complex software workflows.
- Demonstrated experience using AI in QA beyond basic test generation.
- Knowledge of autonomous testing, AI-assisted exploratory testing, or intelligent test orchestration.
- Ability to explain how AI-driven systems determine testing priorities and coverage.
- Experience designing QA frameworks, platforms, or scalable testing solutions is strongly preferred.
- Experience integrating QA processes into CI/CD pipelines at a system level.
- Knowledge of performance testing, reliability testing, or large-scale system validation is an advantage.
- Ability to work remotely with overlap during US Pacific working hours.
Responsibilities
- Design and develop AI-driven QA systems that autonomously explore applications, APIs, workflows, and edge cases.
- Build intelligent testing solutions that generate and execute tests dynamically based on product behavior.
- Create QA agents that integrate with development workflows and provide real-time feedback to engineering teams.
- Establish continuous testing loops that help identify and resolve issues throughout the development lifecycle.
- Validate quality across web applications, backend services, APIs, data flows, and end-to-end user journeys.
- Develop systems for intelligent defect discovery, including functional issues, workflow failures, regressions, and reliability risks.
- Ensure identified issues are reproducible, verified, and clearly communicated to technical stakeholders.
- Build always-on QA capabilities that continuously adapt as products evolve.
- Define quality standards, release readiness criteria, and evidence-based quality thresholds.
- Partner with engineering teams to improve development velocity through proactive quality ownership.
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