JOB OVERVIEW:

As an AVP, Software Development Engineer in Test (SDET) you will join a team of talented developers in test and work on next-generation application software to enhance our engineering best practices, tools, design patterns and frameworks.
This leader will partner with software engineers to understand product architecture and integrate AI-based quality gates and observability into CI/CD pipelines. While creating and maintaining synthetic and production-like data scenarios using AI-powered data generation tools to support comprehensive test coverage.

REQUIREMENTS (WITH AI INTEGRATION)

• Master’s or Bachelor’s Degree in Computer Science or a related field, with a strong foundation in software engineering principles and AI/ML fundamentals.
• Minimum of 10 years of hands-on experience in software development and test automation, with advanced concurrent programming skills in Java/Python and exposure to AI-assisted development tools.
• Minimum of 6 years of leadership experience managing multiple initiatives, including AI-driven quality engineering programs and cross-functional automation strategies.
• Minimum of 4 years of experience designing and building scalable automation frameworks, with a focus on integrating AI/ML for intelligent test generation and maintenance.
• Minimum of 4 years of experience in API testing (SOAP and REST/Microservices), including the use of AI tools for contract validation and anomaly detection.
• Minimum of 4 years of experience with test frameworks such as JUnit, TestNG, and tools for UI/Mobile testing, enhanced by AI-based visual validation and test optimization.

• Collaborate with product, engineering, and cross-functional domain teams to understand features, develop and automate test cases, and drive continuous improvement in product quality and release velocity.
• Apply diverse software testing methodologies—including AI-driven test generation and predictive analytics—to ensure robust and scalable software delivery.
• Coordinate and lead end-to-end (E2E) testing efforts across domains, leveraging AI tools for test orchestration, anomaly detection, and intelligent defect triaging.
• Identify manual processes and implement intelligent automation solutions using AI/ML frameworks to optimize testing efficiency and reduce cycle time.
• Participate in code reviews, advocating for clean code principles and AI-assisted static analysis tools to enhance code quality and maintainability.
• Analyze test results using AI/ML models to detect patterns, predict defect-prone areas, and proactively improve test strategies.
• Document and track bugs to closure, incorporating AI-based prioritization and root cause analysis to streamline defect management.
• Operate within Agile/Scrum frameworks, integrating AI tools for sprint planning, test impact analysis, and continuous feedback loops.
• Mentor and coach teams on best practices in test automation, AI/ML integration in QE, and scalable test architecture design.