QA Engineer

𝗧𝗵𝗶𝘀 𝗿𝗼𝗹𝗲 𝗶𝘀 𝗳𝗼𝗿 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗪𝗲𝗲𝗸𝗱𝗮𝘆'𝘀 𝗰𝗹𝗶𝗲𝗻𝘁𝘀 𝗦𝗮𝗹𝗮𝗿𝘆 𝗿𝗮𝗻𝗴𝗲: 𝗥𝘀 𝟭𝟬𝟬𝟬𝟬𝟬𝟬 - 𝗥𝘀 𝟮𝟱𝟬𝟬𝟬𝟬𝟬 (𝗶𝗲 𝗜𝗡𝗥 𝟭𝟬-𝟮𝟱 𝗟𝗣𝗔) Experience: 4+ yrs Location: Remote (India) As a QA Engineer, you will design, develop, and execute comprehensive test strategies for advanced Generative AI and large language model (LLM) solutions, ensuring they meet strict quality, security, and compliance standards. You will work closely with data scientists, ML engineers, and product managers to validate Agentic AI and Retrieval-Augmented Generation (RAG) workflows, turning complex AI behaviors into measurable test cases. Leveraging your strong command of pytest, Python, and API testing frameworks, you will build robust automation pipelines that seamlessly integrate into continuous integration/continuous deployment (CI/CD) pipelines, while also providing actionable insights to improve model reliability and user experience. Your role will involve monitoring performance metrics, detecting anomalies in generated outputs, and maintaining detailed documentation and dashboards for cross‑functional stakeholders, all while driving the adoption of scalable QA practices across the organization. Requirements 𝗞𝗲𝘆 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 • Define and implement test plans for GenAI, LLM, Agentic AI, and RAG systems • Design and maintain automated test suites using pytest and Python • Develop and execute API tests for model endpoints, data pipelines, and microservices • Perform functional, regression, and performance testing of AI services • Integrate testing into CI/CD pipelines with tools like Jenkins, GitHub Actions, or GitLab CI • Analyze model output quality, bias, and fairness metrics and document findings • Collaborate with ML teams to create test harnesses for new model releases • Create and maintain test data sets, fixtures, and mock services • Report defects, track resolution, and validate fixes to ensure quality standards • Mentor junior testers and promote best practices in AI QA 𝗪𝗵𝗮𝘁 𝗠𝗮𝗸𝗲𝘀 𝗬𝗼𝘂 𝗮 𝗚𝗿𝗲𝗮𝘁 𝗙𝗶𝘁 • 4+ years of QA engineering experience in AI or data science environments • Strong proficiency in pytest, Python, and API testing frameworks (e.g., Requests, RestAssured) • Hands‑on experience with GenAI, LLM, Agentic AI, and RAG testing methodologies • Familiarity with CI/CD pipelines and automation tooling (Jenkins, GitHub Actions) • Deep understanding of AI ethics, bias mitigation, and model explainability • Excellent analytical and problem‑solving skills with a detail‑oriented mindset • Ability to translate complex AI concepts into clear, testable requirements • Strong communication skills for cross‑functional collaboration • Proven track record of delivering high‑quality releases on time • Passion for staying current with emerging AI technologies and testing trends