Quality Engineer- AI Platform Testing

As a Quality Engineer for AI Platform Testing, your role involves contributing to the testing processes of AI-based applications and platforms. Your responsibilities include: - Creating and maintaining comprehensive test cases for web applications - Developing and executing smoke, functional, regression, and integration testing processes - Designing, creating, and maintaining automated test frameworks using tools like RAGA'S/DeepEval/Lang chain, Postman, VS Code, Cucumber, and Node.js - Collaborating closely with development and product teams to ensure testing coverage across all stages of development - Identifying, recording, documenting thoroughly, and tracking bugs - Performing load and performance testing using tools like JMeter - Managing testing processes using GitHub, JIRA, and other DevOps tools - Contributing to continuous integration/continuous deployment (CI/CD) practices to enhance test automation pipelines - Working with cloud technologies, particularly AWS, to manage testing environments Qualifications required for this role include: - 6+ years of relevant work experience in software testing, particularly in automation and test framework development - Proficiency in Python, JavaScript, TypeScript, Postman, VS Code, Cucumber, and Node.js - Experience in Model [LLM's] evaluation testing and analyzing model performance - Strong experience in backend testing with exposure to LLM's using Swagger, Postman tools - Experience in tools like Kubernetes to check the logs - Experience in model evaluation frameworks (e.g., RAGA'S, DeepEval, Lang chain) - Familiarity with various testing techniques, quality standards, and regulations - Strong understanding of version control systems (GitHub) and project management tools (JIRA) - Experience in Agile/Scrum development environments - Detail-oriented approach with excellent problem-solving skills Preferred qualifications include familiarity with continuous integration and delivery (CI/CD) pipelines using GitHub actions, experience or knowledge of machine learning and open-source AI models, test automation using pytest, experience testing applications hosted in multiple geographical locations, and familiarity with cloud technologies, especially AWS.,