Provides technical leadership to Quality Assurance teams responsible for testing software products of moderate to high complexity. Provides supervision and technical guidance in test planning, test design, automation, and performance while ensuring QA standards and best practices are consistently applied across projects. Drive continuous improvement through the adoption of automation, AI-enabled QA practices, and AI-Driven Development Lifecycle approaches. Identifies opportunities to use AI and GenAI tools to improve test scripts creation, requirements analysis, documentation, automation efficiency, and quality insights. Mentors QA team members, reviews test deliverables, and partners with cross-functional teams to ensure software solutions meet functional, technical, and customer quality expectations. ### A Typical Day in the Life Includes: QA Execution and Release Readiness - Align daily QA priorities by reviewing sprint goals, testing assignments, open defects, blockers, release risks, and delivery commitments. - Provide technical guidance in test planning, test design, regression coverage, risk-based testing, and release readiness activities. - Review and validate QA deliverables, including test scripts, performance scenarios, defect reports, test results, and QA documentation. - Perform hands-on testing during the first year, including creating and executing test cases or test scripts as needed to build product familiarity and support project delivery. Automation and Performance Engineering - Lead automation and performance testing initiatives in alignment with the functional roadmap. - Identify automation opportunities, improve test coverage, review script quality, and support scalable testing practices. - Monitor automation results and performance testing outcomes to help identify risks, bottlenecks, and areas for improvement. AI-Enabled QA and AI-DLC Adoption - Embed AI-enabled QA and AI-DLC practices into QA workflows to improve productivity, test coverage, and quality insights. - Leverage AI and GenAI tools to support requirements analysis, test script creation, documentation, defect analysis, automation efficiency, and reporting. - Review and validate AI-generated outputs to ensure accuracy, quality, and alignment with functional and technical requirements. Team Leadership and Stakeholder Communication - Supervise, coach, and mentor QA team members by supporting technical growth, performance feedback, knowledge sharing, and skill development. - Collaborate with Development team and stakeholders to address quality risks and delivery concerns. - Communicate QA status, risks, issues, objectives, initiatives, blockers, and progress against plans to ensure clear stakeholder visibility. Continuous Improvement and Tool Effectiveness - Implement ongoing quality improvement processes by identifying process gaps, applying lessons learned, and promoting consistent QA best practices. - Ensure QA tools used for test management, automation, performance testing, defect tracking, documentation, reporting, and collaboration remain effective and reliable. - Demonstrate learning agility by exploring new tools, technologies, AI capabilities, testing practices, and solutions that improve QA outcomes. - Participate in leadership, transformation, and engagement activities that support employee development, team capability, and continuous improvement. ### Basic Qualifications: - Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field. - 8+ years of experience in software development, product testing, or quality assurance, including at least 3 years in a QA leadership or managerial role. - Experience leading QA teams across manual, automation, and performance testing within Agile/Scrum development environments. - Willing to work on a dayshift schedule and comply with RTO requirements of 3 days per week. Leadership & Management Skills - Proven ability to lead QA teams, mentor team members, foster teamwork, and motivate others effectively. - Strong communication, stakeholder collaboration, problem-solving, interpersonal, and project management skills. - Ability to drive QA strategy, quality initiatives, process improvements, and organizational change. - Strong commitment to software quality, customer satisfaction, product reliability, and continuous improvement. - Demonstrates leadership that inspires confidence, accountability, collaboration, and a culture of quality excellence. Automation & Performance Testing, Frameworks - Hands-on experience creating, maintaining, and executing automation test scripts. - Hands-on experience creating or executing performance test scripts. - Ability to analyze automation and performance test results and support release quality decision. - Experience with API testing. - Experience supporting automation and performance testing within CI/CD pipelines. - Understanding of automation frameworks and design patterns, including Page Object Model. - Exposure to cloud and container technologies. - Ability to apply automation, performance, API, and CI/CD practices to support software quality and delivery. GenAI Competencies - Experience using GenAI tools such as Copilot, AWS Kiro, or similar to support QA activities. - Ability to apply prompt engineering to generate test cases, scripts, documentation, and analysis outputs. - Ability to review, validate, and refine AI-generated outputs to ensure accuracy, quality, and alignment with requirements. ### Preferred Qualifications: Domain & Application Experience - Experience in e-commerce software testing. - Knowledge of web-based, cloud, or multi-tenant application architectures. - Experience developing or supporting AI tools and/or AI-enabled products. - Experience applying GenAI tools to QA activities such as test generation, documentation, defect analysis, and automation support. Defect Tracking & Development Tools - Knowledge of JIRA or similar defect tracking tools. - Familiarity with source control and CI/CD tools such as GitHub and Jenkins. Automation & Performance Testing, Frameworks - Automation Tools: Playwright, Selenium WebDriver, Robot Framework, or similar. - Performance Tools: JMeter, LoadRunner, or equivalent. - API Testing: Postman or similar API testing tools. - Languages: JavaScript, Python, or similar scripting languages. - Frameworks: Page Object Model and advanced automation frameworks. - Cloud & Containers: AWS, Azure, Docker, Kubernetes, or similar. GenAI Competencies - Prompt Engineering: Ability to use tools such as Copilot or AWS Kiro to generate test cases, scripts, and documentation. - AI-Assisted Test Generation: Experience using GenAI to create regression tests, edge cases, and exploratory scenarios. - Code & Script Generation: Ability to leverage AI to support automation script creation and improvement.