AI + Automation QA Engineer

We are seeking an AI + SQE Automation Engineer to join our dynamic team. The ideal candidate will have strong experience in quality engineering, test automation, and AI-assisted testing using tools such as GitHub Copilot, Microsoft Copilot, and AI-enabled test solutions and a proven ability to accelerate test design, optimize regression suites, perform dependency and impact analysis, and improve quality reporting across the SDLC.

Responsibilities:

Analyze business requirements, user stories, APIs, and technical specifications to define comprehensive test strategies.

Design, develop, and maintain automation frameworks for UI, API, integration, and end-to-end testing.

Leverage AI tools to generate and optimize test cases, test data, automation scripts, and documentation.

Perform AI-assisted test impact analysis to determine affected applications, APIs, user stories, and regression scope.

Build reusable automation assets and improve execution efficiency through AI-assisted development.

Validate data quality, business rules, controls, and compliance requirements.

Execute and support continuous testing within CI/CD pipelines.

Analyze application telemetry, logs, and production signals to identify quality risks and testing gaps.

Participate in dependency analysis across repositories, services, APIs, and downstream applications.

Validate and govern AI-generated outputs with human-in-the-loop review practices.

Required Skills & Qualifications:

Quality engineering across functional, integration, regression, API, and risk-based testing in Agile/Scrum environments.

Defect management and root cause analysis with strong analytical skills.

Automation using Selenium, Playwright, or Cypress with Cucumber BDD and TestNG/JUnit.

API automation with REST Assured or equivalent tools and sound test data management.

AI skills including GitHub Copilot, Microsoft Copilot, prompt engineering, AI-assisted test generation and automation, AI-driven defect analysis, regression optimization, human-in-the-loop validation, and responsible AI practices.

Technical proficiency in Java, JavaScript, or Python, SQL and data validation, Git/Bitbucket, and CI/CD tools such as Jenkins, GitHub Actions, or Azure DevOps.

Experience with API testing tools and log analysis or monitoring tools.