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About the role
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.
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.
Preferred:
GenAI, prompt engineering, RAG, AI agents
MLOps / LLMOps knowledge
Cloud (AWS/Azure/GCP)
Vector DBs, embeddings, AI orchestration
Predictive analytics & self-healing automation
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