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.
• 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