Onsite QE LEAD

Job Description Define and execute QE transformation roadmap (manual → automation → AI-driven) Establish test strategy across UI, API, data, and AI layers Drive test automation adoption (Selenium, Cypress, Playwright, etc.) Integrate quality into CI/CD pipelines (Jenkins, GitHub Actions, Azure DevOps) Partner with engineering, product, and data teams to embed quality early Own test coverage, defect leakage, and release quality metrics Lead QE team (delivery + coaching + performance oversight) We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to HR@insightglobal.com.

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https://insightglobal.com/workforce-privacy-policy/. Skills and Requirements Must-Have Experience 6+ years in Quality Engineering / QA Leadership Proven experience as QE Lead or Test Architect Background in retail/eCommerce or high-scale digital platforms (ideal: Sephora-type environment) Experience leading onshore/offshore QE teams

AI / XQE

Capabilities (Differentiator) Experience implementing AI-driven testing frameworks (self-healing test automation, test case generation, defect prediction) Exposure to GenAI tools (ChatGPT, Copilot, or internal LLMs) for: Test creation Test data generation Intelligent defect triage Understanding of testing AI/ML models, including: Model validation Bias / drift considerations Data quality testing Familiarity with XQE concepts: Autonomous testing Continuous quality engineering Shift-left + shift-right strategies Core Responsibilities