– QA Manager | Automation & AI

nPosition: QA Manager / QA Engineering Manager

nExperience: 13+ Years

nEmployment Type: Full-Time

nWork Location: (Location)

nWork Mode: (Hybrid / Work From Office / Remote)

nAbout the Role

nWe are looking for an experienced and technically solid QA Manager with 13+ years of experience in software quality assurance, test automation, and quality engineering.

nThe 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.

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

nKey Responsibilities

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- Define and implement the overall QA and Quality Engineering strategy across products and applications.
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- Lead and mentor QA engineers, automation engineers, and SDET teams.
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- Drive the design, development, and maintenance of robust test automation frameworks .
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- Establish automation standards, best practices, coding guidelines, and reusable testing components.
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- Identify opportunities to increase automation coverage and reduce manual testing efforts.
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- Design and execute strategies for functional, regression, integration, API, UI, performance, and end-to-end testing .
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- Integrate automated testing into CI/CD pipelines and support continuous quality practices.
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- Work closely with Engineering, Product, DevOps, and other stakeholders to ensure quality throughout the SDLC.
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- Define QA metrics, quality gates, test coverage, defect leakage, automation coverage, and release-quality KPIs.
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- Drive root-cause analysis of critical production defects and implement preventive quality measures.
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- Evaluate and introduce modern testing tools, frameworks, and methodologies.
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- 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.
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- Explore and implement AI-powered testing tools and solutions to improve QA productivity and test effectiveness.
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- Evaluate the use of LLMs,



AI agents, and AI-assisted development/testing workflows within the QA lifecycle.
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- Establish best practices for testing AI/ML-based applications where applicable.
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- Ensure adequate test planning, risk assessment, release readiness, and quality governance.
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- Collaborate with engineering leadership to improve overall software reliability and engineering quality.
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nMandatory Technical Skills

nAutomation Testing – Mandatory

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

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

nExperience in areas such as:

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

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

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

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- Lead and develop a high-performing QA/Quality Engineering team.
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- Set technical direction for automation and AI-driven testing.
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- Conduct technical reviews and establish engineering best practices.
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- Mentor senior QA engineers and automation specialists.
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- Define team objectives, delivery expectations, and quality standards.
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- Partner with senior engineering and product leadership on quality initiatives.
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- Drive continuous improvement across the QA organization.
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nRequired Experience

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

nThe ideal candidate will be someone who:

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

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- Automation coverage and reliability
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- Reduction in regression-testing effort
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- Defect detection and prevention
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- Production defect leakage
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- Test execution efficiency
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- CI/CD quality-gate adoption
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- Release quality and stability
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- Adoption and measurable impact of AI-driven testing initiatives
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📌 Quality Assurance Manager (Pushkar)
🏢 Questhiring
📍 Pushkar