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 ResponsibilitiesStrong 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) flowsCollaborate with developers, product owners, and other QA engineers in Agile teams.


Must-Have SkillsDeep experience in Functional Testing:

o Advanced REST API Testing (Postman, REST Assured, etc.)
o Integration, Acceptance, E2E, Smoke, Sanity, System TestingStrong foundation in Non-Functional Testing:

o Performance and Load Testing
o Stress and Security TestingProficiency in Testing Methods:

o Black-box, White-box, Grey-box TestingRegression 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.


QualificationsBachelor's or master's in computer science, Engineering, or related field. * Certifications in testing or security are a plus (e.g., ISTQB, OWASP).