Quality Assurance Manager

Questhiring · India

Job Description – QA Manager | Automation & AI

Position: QA Manager / QA Engineering Manager

Experience: 13+ Years

Employment Type: Full-Time

Work Location: [Location]

Work Mode: [Hybrid / Work From Office / Remote]

About the Role

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 should also have 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.

This role requires a combination of technical depth, QA leadership, automation expertise, and AI knowledge .

Key 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 optimization, 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.

Mandatory Technical Skills

Automation Testing – Mandatory

• Strong hands-on experience in Test Automation .
• Experience designing and implementing scalable automation frameworks.
• Strong experience with tools/frameworks such as:
• Selenium
• Playwright / Cypress
• Appium
• REST Assured / API Automation
• PyTest / JUnit / TestNG
• Strong programming experience in Java, Python, JavaScript, or similar languages .
• Experience with UI, API, integration, regression, and end-to-end automation.

AI – Mandatory

Candidates must have practical exposure to AI/Generative AI in QA or software engineering .

Experience in areas such as:

• Generative AI for software testing
• AI-assisted test-case generation
• AI-based test automation
• LLM-based testing solutions
• AI-powered defect analysis
• AI-generated test data
• AI agents for QA/testing workflows
• Prompt engineering
• Integration of AI tools into QA processes
• Testing of AI/ML-based applications
• Tools/platforms leveraging LLMs for software quality

CI/CD & DevOps

• Strong understanding of CI/CD pipelines.
• Experience with Jenkins, GitHub Actions, GitLab CI/CD, Azure DevOps, or similar tools.
• Experience integrating automated test suites into CI/CD pipelines.
• Good understanding of Git and modern DevOps practices.
• Exposure to cloud environments such as AWS, Azure, or GCP is preferred.

Additional Skills

• Strong understanding of Agile/Scrum methodologies .
• Experience with defect-management and test-management tools such as Jira, Azure DevOps, Zephyr, TestRail, or similar.
• Good understanding of SDLC/STLC and software quality processes.
• Experience with performance and security testing is an added advantage.
• Strong analytical and problem-solving skills.
• Excellent communication and stakeholder-management skills.

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 organization.

Required Experience

• 13+ years of overall experience in Software Testing / Quality Engineering.
• Strong experience in Test Automation and Automation Framework Development .
• Proven experience leading QA/Automation teams.
• Hands-on experience with AI/Generative AI is mandatory.
• Experience implementing automation at scale in enterprise or product environments.
• Strong programming and scripting skills.
• Experience working in Agile development environments.

Preferred Candidate Profile

The ideal candidate will be someone who:

• Has progressed from hands-on QA/Automation engineering into QA leadership.
• Remains technically hands-on and can review or contribute to automation frameworks.
• Has successfully led large-scale automation initiatives.
• Has practical experience applying AI/GenAI to software testing and quality engineering .
• Can balance people leadership with strong technical ownership.
• Is comfortable working with Engineering, Product, DevOps, and senior leadership.

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