Qentelli - AI Quality Assurance Lead Engineer - Generative AI

About the Role : We are seeking an innovative AI Engineer to drive the adoption of Generative AI and Agentic AI across our Quality Engineering. This role will partner with Manual QA, Automation Engineering, and Product teams to integrate AI capabilities into existing test automation frameworks and build intelligent AI agents that streamline software testing activities. The ideal candidate will design and develop AI-powered solutions for automated test case generation, test data creation, script generation, intelligent defect analysis, self-healing automation, and seamless integration with enterprise tools such as JIRA, test management platforms, and CI/CD pipelines. The role will play a key part in modernizing QA processes through autonomous and AI-assisted testing capabilities. Key Responsibilities : - Partner with Manual QA and Automation Engineering teams to integrate Generative AI and Agentic AI capabilities into existing test automation frameworks. - Design, develop, and maintain AI agents to automate test case generation, test data preparation, test script creation, and regression test optimization. - Build AI-powered solutions for self-healing automation, intelligent failure analysis, root cause identification, and automated script maintenance. - Integrate Large Language Models (LLMs) with automation frameworks to enable natural language-driven test generation and execution. - Develop AI-driven integrations with enterprise tools such as JIRA, test management platforms, and CI/CD pipelines for automated defect creation, test result publishing, and reporting. - Create reusable AI services, prompts, and workflows to improve QA productivity, reduce manual effort, and standardize AI adoption across projects. - Collaborate with Product, Development, DevOps, and QA teams to identify opportunities for applying AI throughout the Software Development and Testing Lifecycle. - Implement Retrieval-Augmented Generation (RAG), vector databases, and prompt engineering techniques to leverage enterprise knowledge for intelligent QA assistance. - Monitor, evaluate, and continuously improve AI models and agent performance using quality metrics, automation KPIs, and user feedback. - Establish best practices, governance, and security standards for responsible and scalable AI adoption within the Quality Engineering organization. Required Qualifications : - Experience integrating AI capabilities into automation frameworks, including self-healing automation, intelligent test generation, and AI-driven test optimization to improve productivity, resilience, and test coverage. - Hands-on experience in mobile application testing, including Android and iOS platforms, with expertise in automation frameworks such as Appium. Strong understanding of mobile test strategies, device compatibility testing, mobile APIs, and integration of AI capabilities into mobile automation to improve test coverage and efficiency. - 4+ years of experience in Python development with strong knowledge of APIs, asynchronous programming, and concurrency. - Hands-on experience with LangChain, LangGraph, LangSmith, and LLM APIs (OpenAI, Claude, Gemini, etc.). - Strong expertise in Prompt Engineering, RAG, vector databases, embeddings, and AI agent development. - Experience integrating AI agents with enterprise applications through REST APIs, databases, and SDKs. - Good understanding of Git, CI/CD, Docker, cloud platforms (AWS/Azure/GCP), and software engineering best practices. - Knowledge of multi-agent architectures, AI evaluation frameworks, and LLM application testing. - Strong understanding of Payments domain, including payment gateways, transaction lifecycle, settlements, and reconciliation. - Good experience in Selenium automation, API testing, and software testing concepts.