Quality Assurance/Test Engineer - Automation Testing

Role Overview: As a Test Engineer for AI-powered systems, your role is crucial in ensuring the quality, scalability, and reliability of various AI applications, including Bedrock Agent workflows, forecasting systems, conversational interfaces, and API integrations. You will be responsible for building and maintaining automated test suites, validating forecasting accuracy, testing conversational AI flows, and collaborating with engineering teams to improve quality and strengthen test coverage. Key Responsibilities: - Build and maintain automated test suites for AWS Bedrock Agent Actions, model invocation workflows, and API integrations - Validate forecasting model accuracy against historical datasets and expected business outcomes - Test conversational AI flows, edge cases, fallback scenarios, and error handling mechanisms - Conduct performance, scalability, and load testing across backend services and AI systems - Create and maintain regression test suites for model updates and platform enhancements - Develop automated API validation and integration testing pipelines - Collaborate with engineering and AI teams to identify defects, improve quality, and strengthen test coverage - Validate security, reliability, and resiliency of distributed AI-powered applications - Monitor testing metrics and ensure continuous quality improvements across releases Qualification Required: - Hands-on experience with test automation frameworks such as DeepEval, Playwright, Pytest, and Jest - Strong understanding of ML/AI model evaluation metrics and validation techniques - Experience with API testing tools such as Postman and REST Assured - Performance and load testing experience using JMeter, Locust, or similar tools - Strong understanding of regression testing, integration testing, and end-to-end testing methodologies - Experience testing REST APIs, asynchronous workflows, and distributed systems - Knowledge of conversational AI testing, prompt validation, and response quality assessment - Familiarity with CI/CD pipelines and automated quality gates Additional Company Details: The company focuses on the development and validation of AI-powered systems, emphasizing the importance of quality engineering and automation. The ideal candidate is expected to have a strong QA/Test engineering background with AI/GenAI systems, a passion for quality engineering, experience in validating scalable AI-powered applications, attention to detail, analytical mindset, and comfort working in agile, fast-paced engineering environments. Role Overview: As a Test Engineer for AI-powered systems, your role is crucial in ensuring the quality, scalability, and reliability of various AI applications, including Bedrock Agent workflows, forecasting systems, conversational interfaces, and API integrations. You will be responsible for building and maintaining automated test suites, validating forecasting accuracy, testing conversational AI flows, and collaborating with engineering teams to improve quality and strengthen test coverage. Key Responsibilities: - Build and maintain automated test suites for AWS Bedrock Agent Actions, model invocation workflows, and API integrations - Validate forecasting model accuracy against historical datasets and expected business outcomes - Test conversational AI flows, edge cases, fallback scenarios, and error handling mechanisms - Conduct performance, scalability, and load testing across backend services and AI systems - Create and maintain regression test suites for model updates and platform enhancements - Develop automated API validation and integration testing pipelines - Collaborate with engineering and AI teams to identify defects, improve quality, and strengthen test coverage - Validate security, reliability, and resiliency of distributed AI-powered applications - Monitor testing metrics and ensure continuous quality improvements across releases Qualification Required: - Hands-on experience with test automation frameworks such as DeepEval, Playwright, Pytest, and Jest - Strong understanding of ML/AI model evaluation metrics and validation techniques - Experience with API testing tools such as Postman and REST Assured - Performance and load testing experience using JMeter, Locust, or similar tools - Strong understanding of regression testing, integration testing, and end-to-end testing methodologies - Experience testing REST APIs, asynchronous workflows, and distributed systems - Knowledge of conversational AI testing, prompt validation, and response quality assessment - Familiarity with CI/CD pipelines and automated quality gates Additional Company Details: The company focuses on the development and validation of AI-powered systems, emphasizing the importance of quality engineering and automation. The ideal candidate is expected to have a strong QA/Test engineering background with AI/GenAI systems, a passion for quality engineering, experience in validating scalable AI-powered applications, attention to det