Senior Software Development Engineer in Testing

Job Description

Job Title: SDET (Software Development Engineer in Testing) – Senior Automation QA & Manual Testing Experience: 8+ Years Employment Type: Full-Time Dept: FSD Designation: Senior Specialist – FSD QA Location: Hybrid About Us At InxiteOut Pvt. Ltd., we specialize in building next-generation AI-powered applications that solve real-world business problems. We are expanding our QA team to ensure our AI systems are robust, secure, and aligned with user expectations. Key Responsibilities Strong knowledge of Python. Configure and test AI/ML product/system. Lead the quality assurance strategy and establish automation frameworks from scratch. Manage and mentor junior QA engineers and guide them in test automation and AI system validation. Own functional and non-functional testing across complex distributed systems. Own API, integration, acceptance, system, and end-to-end (E2E) testing. Lead test planning, test case design, and execution for web, API, and AI-based systems. Design and maintain regression test suites and ensure full coverage. Collaborate with Full Stack Developers, Data Scientists, and AI Engineers to define test strategies for LLM-based features. Own testing of AI-specific behaviors including: o Hallucination and factual accuracy o Consistency in prompt-response pairs o Retrieval-Augmented Generation (RAG) flows Collaborate with developers, product owners, and other QA engineers in Agile teams. Must-Have Skills Deep experience in Functional Testing: o Advanced REST API Testing (Postman, REST Assured, etc.) o Integration, Acceptance, E2E, Smoke, Sanity, System Testing Strong foundation in Non-Functional Testing: o Performance and Load Testing o Stress and Security Testing Proficiency in Testing Methods: o Black-box, White-box, Grey-box Testing Regression Testing lifecycle ownership Expertise in automation frameworks (e.g., Selenium, Playwright, Cypress, TestNG) Strong scripting/coding skills in Python (preferable) / Java / JavaScript Familiarity with containerized environments (Docker, Kubernetes) Good-to-Have (AI Testing Focus) Exposure to testing AI/ML products/systems. Experience of integrating tests into CI/CD pipelines (GitHub Actions, GitLab, Azure DevOps etc.). Hands-on experience with AI testing tools or custom frameworks. Familiarity with evaluating LLM (large language model) outputs. Experience of validating RAG-based architecture. Understanding of hallucination detection metrics and prompt tuning. Exposure to safety audits for AI applications. Collaboration experience with Full Stack, Data Science, Data Engineering & DevOps teams. Qualifications Bachelor’s or master’s in computer science, Engineering, or related field. Certifications in testing or security are a plus (e.g., ISTQB, OWASP).