JOB SUMMARY We are seeking a Principal Software Quality Engineer to lead and drive our quality assurance efforts across web, mobile, and AI/ML-powered applications. This role is pivotal in ensuring the reliability, performance, scalability, and intelligence of our supply chain management platform. You will architect innovative testing strategies, incorporating cutting-edge ML/AI tools, to proactively identify issues and enhance our automated testing capabilities. Your leadership will ensure that our platform continues to set industry standards in quality and reliability. Responsibilities: * Test Automation Frameworks: * Architect and implement advanced, scalable test automation frameworks for web, mobile, and AI/ML-powered applications. * Ensure seamless integration of these frameworks with Kubernetes-based microservices. * Device Farm Management: * Design and manage cloud-based and on-premises device farms to enable robust testing across various mobile devices, operating systems, and configurations. * Comprehensive Testing Strategies: * Develop testing strategies that encompass functional, performance, security, regression, and AI/ML validation for complex supply chain systems. * Leverage AI/ML algorithms to generate dynamic and intelligent test cases, predict potential issues, and enhance test execution efficiency. * CI/CD and Quality Gates: * Implement CI/CD pipelines with rigorous quality gates to uphold high standards in software delivery. * AI/ML Testing Integration: * Design and execute test plans for AI/ML models, focusing on aspects such as accuracy, bias, scalability, and drift. * Integrate ML-driven tools to monitor and validate AI system behavior in real-time. * Mentorship and Collaboration: * Mentor and guide QA team members in adopting modern testing methodologies and ML/AI-based tools. * Collaborate with cross-functional teams, including development, data science, and product management, to ensure quality throughout the development lifecycle. * Risk-Based Testing: * Implement risk-based testing strategies to optimize resources and prioritize high-impact scenarios in supply chain operations. * Quality Metrics and Reporting: * Establish and monitor metrics to track quality trends, identify improvement areas, and communicate results to stakeholders. What You Bring: Core QA Expertise: * 12+ years of experience in software quality assurance, with strong expertise in web, mobile, and AI/ML application testing. * Proven expertise in test automation tools such as Selenium, Appium, TestNG, and AI/ML-driven frameworks. Programming and Automation: * Strong programming skills in Java and Python; familiarity with JavaScript or Groovy is a plus. * Hands-on experience designing custom test automation frameworks tailored to business needs. Device Farms and Cloud Platforms: * Expertise in setting up and managing device farms using platforms like BrowserStack, Sauce Labs, or AWS Device Farm. * Deep understanding of cloud platforms (AWS, GCP, or Azure) and containerized environments (Kubernetes). AI/ML Testing: * Experience in validating AI/ML models, including performance, accuracy, fairness, and bias testing. * Familiarity with AI-driven testing tools (e.g., Test.ai, Applitools) and predictive analytics to enhance quality assurance processes. Continuous Integration and DevOps: * Proficiency in CI/CD tools and workflows, including integration of quality gates and automated testing pipelines. Analytical Skills: * Strong analytical skills with a data-driven approach to problem-solving and decision-making. Nice to Have: * Stay updated with the latest trends in QA, AI/ML technologies, and supply chain innovations, applying these advancements to testing processes. * Develop and automate performance testing strategies for supply chain applications under high-load conditions. * Implement intelligent security testing protocols to ensure compliance with industry standards. * Innovate test data management strategies using synthetic data generation and AI/ML to maintain data privacy while ensuring test coverage. * Continuously evaluate and implement tools, frameworks, and processes to enhance quality assurance efficiency.