Software Engineer in Test

We are seeking an experienced SDET – Automation with 2 to 4 years of hands-on experience in building scalable automation solutions and a strong interest or experience in Generative AI–driven testing approaches. The ideal candidate combines solid engineering fundamentals with modern QA practices, including AI-assisted test design, validation, and quality engineering in Agile environments. Key Responsibilities Design, develop, and maintain robust automation frameworks for Web, API, and Mobile applications Build and execute end-to-end automated test suites covering functional, regression, and integration scenarios Apply GenAI techniques to improve test coverage, test data generation, and exploratory testing Validate AI/ML-powered features, including GenAI outputs for accuracy, relevance, bias, and consistency Collaborate with developers, product, data, and DevOps teams to ensure quality across SDLC Automate API and backend testing including validation of data pipelines and services Integrate automation with CI/CD pipelines and ensure reliable test execution Analyze failures, identify root causes, and drive defect resolution (with GenAI is a plus here) Contribute to test strategy, quality metrics, and release readiness Required Skills & Qualifications Experience 6–8 years of experience in Automation Testing / SDET roles Proven experience building or enhancing automation frameworks from scratch Programming & Automation Strong coding skills in Java / Python / JavaScript Hands-on experience with Selenium / Playwright / Cypress Experience with API automation using RestAssured / Postman / Karate Strong understanding of OOP, data structures, and design patterns Generative AI / AI Testing Working knowledge of Generative AI concepts (LLMs, prompts, embeddings, hallucinations, evaluation metrics) Experience with Claude-Code is advantage Experience testing GenAI-enabled features such as chatbots, summarization, recommendation, or content generation Hands-on exposure to prompt engineering, prompt versioning, and prompt evaluation Experience validating LLM outputs for correctness, relevance, safety, bias, and determinism Familiarity with LLM APIs (e.g., OpenAI, Azure OpenAI, or similar) Understanding of AI testing strategies including golden datasets, synthetic data generation, and regression testing for AI models