Job Summary

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.

Job Requirements

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Define and execute the AI-based QA strategy across projects.

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Design and develop scalable AI-enabled automation frameworks.

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Implement AI/GenAI use cases such as:

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Test case & script generation

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Defect analysis & prediction

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Risk-based and intelligent testing

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Test data generation

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Build self-healing automation, smart reporting, and predictive analytics solutions.

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Evaluate and integrate AI tools, LLMs, and QA platforms.

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Collaborate with cross-functional teams (QA, Dev, Product, DevOps).

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Establish best practices, governance, and reusable QA components.

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Develop POCs and scale them to enterprise-level solutions.

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Define and track automation and AI effectiveness metrics.

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Mentor teams on AI-driven QA practices.

Education

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10+ years in QA, automation, or architecture roles.

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Strong experience in test automation frameworks (Selenium, Playwright, Cypress, Appium, REST Assured, etc.).

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Solid understanding of AI/ML/GenAI concepts applied to QA.

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Hands-on experience with LLM-based solutions (test generation, log analysis, etc.).

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Programming expertise in Python, Java, or JavaScript.

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Experience with CI/CD tools (Jenkins, GitHub Actions, Azure DevOps, etc.).

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Knowledge of API, UI, performance, security testing, and test data management.

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Ability to design and integrate AI-first QA solutions.

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Robust architecture, problem-solving, and stakeholder management skills.

Preferred:

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GenAI, prompt engineering, RAG, AI agents

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MLOps / LLMOps knowledge

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Cloud (AWS/Azure/GCP)

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Vector DBs, embed

📌 Senior Software QA Engineer (Bengaluru)
🏢 NetApp
📍 Bengaluru