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