Quality Assurance Manager

Role Overview We are seeking a QA Manager with strong experience in test automation and a passion for AI . You will lead a team of QA engineers and work closely with cross-functional stakeholders to build robust testing frameworks, introduce intelligent automation, and ensure end-to-end product quality. You’ll also play a key role in shaping how AI can be used to improve QA efficiency and software reliability. Key Responsibilities Own the QA strategy, test planning, and execution for web, mobile, and API-based applications. Lead, mentor, and grow a team of QA engineers and SDETs. Design and implement automation frameworks using modern tools (e.g., Selenium, Cypress, Playwright, Appium). Evaluate and integrate AI-driven QA tools (e.g., Testim, Mabl, Functionize, Diffblue, ChatGPT-based test case generation). Drive continuous integration and delivery (CI/CD) of automated tests across environments. Establish test data strategies using synthetic data generation and AI-based test data tools. Collaborate with product managers, developers, and DevOps teams to define acceptance criteria and promote shift-left testing. Monitor quality metrics and use analytics to improve test coverage, defect detection, and release velocity. Stay abreast of emerging QA trends, especially in AI/ML validation, generative AI testing, and model interpretability QA. Required Skills & Qualifications 8+ years of experience in Quality Assurance, with at least 3 years in a managerial or leadership role. Proven track record in building and scaling test automation for complex systems. Experience with at least one programming language (Python, Java, JavaScript preferred). Hands-on experience with AI-powered QA tools or building AI/ML pipelines with embedded QA. Solid understanding of AI/ML concepts such as model training, inference, data drift, and validation. Strong knowledge of testing practices: unit, integration, functional, performance, regression, security. Experience with CI/CD tools (Jenkins, GitHub Actions, GitLab, etc.). Familiarity with cloud platforms (AWS, Azure, or GCP) and containerized environments (Docker, Kubernetes). Excellent leadership, communication, and stakeholder management skills. Nice to Have Exposure to MLOps or AI model lifecycle QA. Experience in regulatory or enterprise-level compliance QA (e.g., SOC2, GDPR). Contributions to open-source QA projects or AI QA communities. Show more Show less