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
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.

Education
10+ years in QA, automation, or architecture roles.
Robust 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.
Robust architecture, problem-solving, and stakeholder management skills.

Preferred:
GenAI, prompt engineering, RAG, AI agents
MLOps / LLMOps knowledge
Cloud (AWS/Azure/GCP)
Vector DBs, embed

📌 Senior Software Qa Engineer Bengaluru (India)
🏢 NetApp
📍 India