Role Overview :

We are looking for an experienced and technically strong QA Manager with 13+ years of experience in software quality assurance, test automation, and quality engineering. The ideal candidate should have strong hands-on expertise in Automation Testing and practical experience working with AI/Generative AI technologies in software testing or quality engineering. The candidate will be responsible for defining QA strategy, driving automation initiatives, improving test coverage, and leading the adoption of AI-driven testing practices.

Responsibilities :

- Define and implement the overall QA and Quality Engineering strategy across products and applications.

- Lead and mentor QA engineers, automation engineers, and SDET teams.

- Drive the design, development, and maintenance of robust test automation frameworks.

- Establish automation standards, best practices, coding guidelines, and reusable testing components.

- Identify opportunities to increase automation coverage and reduce manual testing efforts.

- Design and execute strategies for functional, regression, integration, API, UI, performance, and end-to-end testing.

- Integrate automated testing into CI/CD pipelines and support continuous quality practices.

- Work closely with Engineering, Product, DevOps, and other stakeholders to ensure quality throughout the SDLC.

- Define QA metrics, quality gates, test coverage, defect leakage, automation coverage, and release-quality KPIs.

- Drive root-cause analysis of critical production defects and implement preventive quality measures.

- Evaluate and introduce modern testing tools, frameworks, and methodologies.

- Lead the adoption of AI/Generative AI in Software Testing, including AI-assisted test generation, test-case optimisation, defect analysis, test-data generation, and intelligent automation.

- Explore and implement AI-powered testing tools and solutions to improve QA productivity and test effectiveness.

- Evaluate the use of LLMs, AI agents, and AI-assisted development/testing workflows within the QA lifecycle.

- Establish best practices for testing AI/ML-based applications where applicable.

- Ensure adequate test planning, risk assessment, release readiness, and quality governance.

- Collaborate with engineering leadership to improve overall software reliability and engineering quality.

Leadership Responsibilities :

- Lead and develop a high-performing QA/Quality Engineering team.

- Set technical direction for automation and AI-driven testing.

- Conduct technical reviews and establish engineering best practices.

- Mentor senior QA engineers and automation specialists.

- Define team objectives, delivery expectations, and quality standards.

- Partner with senior engineering and product leadership on quality initiatives.

- Drive continuous improvement across the QA organisation.

Mandatory Technical Skills :

Automation Testing :

- Strong hands-on experience in Test Automation and designing scalable automation frameworks.

- Proficiency in tools/frameworks : Selenium, Playwright / Cypress, Appium, REST Assured / API Automation, PyTest / JUnit / TestNG.

- Strong programming experience in Java, Python, JavaScript, or similar languages.

AI/GenAI :

- Practical exposure to AI/Generative AI in QA, including AI-assisted test-case generation, LLM-based testing solutions, AI-powered defect analysis, and AI agents for QA workflows.

CI/CD and DevOps :

- Strong understanding of CI/CD pipelines (Jenkins, GitHub Actions, GitLab CI/CD, Azure DevOps).

- Experience integrating automated test suites into CI/CD pipelines.

Additional Skills :

- Strong understanding of Agile/Scrum methodologies.

- Experience with defect-management and test-management tools (Jira, Azure DevOps, Zephyr, TestRail).

- Strong analytical, problem-solving, and stakeholder-management skills.

Key Success Metrics :

- Automation coverage and reliability.

- Reduction in regression-testing effort.

- Defect detection and prevention.

- Production defect leakage.

- Test execution efficiency.

- CI/CD quality-gate adoption.

- Release quality and stability.

- Adoption and measurable impact of AI-driven testing initiatives.