Senior Software QA Engineer

NetApp · Bengaluru · 10+ yrs experience · Posted 2026-07-18

Tech stack: AWS, Azure, GCP, GitHub Actions, Java, JavaScript, Jenkins, Python

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About the role

Lead the design and implementation of an AI-driven QA strategy to transform testing processes.
Architect and build next-generation QA frameworks leveraging AI/ML and Generative AI.
Drive innovation across test automation, defect prediction, and test optimization to improve quality, efficiency, and speed.
Act as a technical leader and advisor, enabling teams to adopt AI-powered testing solutions at scale.
Qualifications: - Define and execute the AI-based QA strategy across projects.
- Design and develop scalable AI-enabled automation frameworks.
- Implement AI/GenAI use cases such as:
- Test case & script generation
- Defect analysis & prediction
- Risk-based and intelligent testing
- Test data generation
- Build self-healing automation, smart reporting, and predictive analytics solutions.
- Evaluate and integrate AI tools, LLMs, and QA platforms.
- Collaborate with cross-functional teams (QA, Dev, Product, DevOps).
- Establish best practices, governance, and reusable QA components.
- Develop POCs and scale them to enterprise-level solutions.
- Define and track automation and AI effectiveness metrics.
- Mentor teams on AI-driven QA practices.
- 10+ years in QA, automation, or architecture roles.
- Strong experience in test automation frameworks (Selenium, Playwright, Cypress, Appium, REST Assured, etc.).
- Solid understanding of AI/ML/GenAI concepts applied to QA.
- Hands-on experience with LLM-based solutions (test generation, log analysis, etc.).
- Programming expertise in Python, Java, or JavaScript.
- Experience with CI/CD tools (Jenkins, GitHub Actions, Azure DevOps, etc.).
- Knowledge of API, UI, performance, security testing, and test data management.
- Ability to design and integrate AI-first QA solutions.
- Strong architecture, problem-solving, and stakeholder management skills.
- GenAI, prompt engineering, RAG, AI agents
- MLOps / LLMOps knowledge Cloud (AWS/Azure/GCP)
- Vector DBs, embeddings, AI orchestration
- Predictive analytics & self-healing automation

Qualifications

- Define and execute the AI-based QA strategy across projects.
- Design and develop scalable AI-enabled automation frameworks.
- Implement AI/GenAI use cases such as:
- Test case & script generation
- Defect analysis & prediction
- Risk-based and intelligent testing
- Test data generation
- Build self-healing automation, smart reporting, and predictive analytics solutions.
- Evaluate and integrate AI tools, LLMs, and QA platforms.
- Collaborate with cross-functional teams (QA, Dev, Product, DevOps).
- Establish best practices, governance, and reusable QA components.
- Develop POCs and scale them to enterprise-level solutions.
- Define and track automation and AI effectiveness metrics.
- Mentor teams on AI-driven QA practices.
- 10+ years in QA, automation, or architecture roles.
- Strong experience in test automation frameworks (Selenium, Playwright, Cypress, Appium, REST Assured, etc.).
- Solid understanding of AI/ML/GenAI concepts applied to QA.
- Hands-on experience with LLM-based solutions (test generation, log analysis, etc.).
- Programming expertise in Python, Java, or JavaScript.
- Experience with CI/CD tools (Jenkins, GitHub Actions, Azure DevOps, etc.).
- Knowledge of API, UI, performance, security testing, and test data management.
- Ability to design and integrate AI-first QA solutions.
- Strong architecture, problem-solving, and stakeholder management skills.
- GenAI, prompt engineering, RAG, AI agents
- MLOps / LLMOps knowledge Cloud (AWS/Azure/GCP)
- Vector DBs, embeddings, AI orchestration
- Predictive analytics & self-healing automation

Responsibilities

- Lead the design and implementation of an AI-driven QA strategy to transform testing processes.
- Architect and build next-generation QA frameworks leveraging AI/ML and Generative AI.
- Drive innovation across test automation, defect prediction, and test optimization to improve quality, efficiency, and speed.
- Act as a technical leader and advisor, enabling teams to adopt AI-powered testing solutions at scale.

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