Global QE Lead: AI-Driven Testing & Quality Strategy

Eli Lilly and Company; South San Francisco (CA); On-site; USD 71,000 - 169,000; Full time

Benefits offered by this job

401(k) plan

Pension

Healthcare benefits

Vacation benefits

Well-being benefits

Job summary

Lilly is seeking a Quality Engineering Lead to own the end-to-end testing lifecycle for software products, from strategy through release. You’ll lead a globally distributed QE team, define testing requirements, automate, and drive quality across product areas while sustaining US business-hour coverage as needed.

You will partner with developers, product managers, and analysts to plan tests, execute, and improve release readiness, incorporating AI-assisted testing and modern automation to enhance

Qualifications

• 8+ years of software quality assurance experience.
• Experience leading end-to-end testing in Agile environments.
• Proficiency with test management and automation tools.
• Authorized to work in the United States full-time.

Responsibilities

• Lead end-to-end testing lifecycle (STLC) and risk mitigation.
• Define testing requirements, strategies, and automation plans.
• Coordinate SIT and UAT across multiple teams and time zones.
• Drive CI/CD integration and AI-assisted testing initiatives.
• Provide regular test progress updates to stakeholders.

Skills

Software QA leadership

End-to-end testing

Agile / Jira

AI in testing

Test planning

Cross-team communication

Education

Bachelor’s degree in CS/IT or related field

Tools

TestRail

XRay

Qtest

Quality Center

Selenium

Cypress

Playwright

Puppeteer

GitHub Actions

Jenkins

Job description

Lilly is seeking a Quality Engineering Lead to own the end-to-end testing lifecycle for software products, from strategy through release. You’ll lead a globally distributed QE team, define testing requirements, automate, and drive quality across product areas while sustaining US business-hour coverage as needed.

You will partner with developers, product managers, and analysts to plan tests, execute, and improve release readiness, incorporating AI-assisted testing and modern automation to enhance