A quality assurance engineer is needed to design and implement end-to-end test strategies across multiple layers of the testing pyramid. This position involves defining model validation criteria, driving automation of QA processes, and leading validation of integration points between AI components and business logic. Mandatory qualifications include a bachelor's degree in computer science or a related field, 2–3 years of experience in AQA, software test engineering, or related roles, including leadership responsibilities, and solid knowledge of the software development life cycle (SDLC), Agile methodologies, and QA/QE best practices. Experience building and scaling test automation frameworks, strong technical skills in working with REST APIs, data pipelines, backend services, and cloud platforms, exceptional attention to detail, and demonstrated ability to learn quickly are also required. Nice-to-have requirements include experience testing AI/ML systems in production environments, familiarity with GenAI model evaluation metrics, and experience setting up proactive monitoring and alerting systems to detect model drift, performance degradation, or workflow failures. Key Responsibilities: - Design and implement end-to-end test strategies - Define model validation criteria - Drive automation of QA processes - Lead validation of integration points Required Skills: - Bachelor's degree in computer science - 2-3 years of experience in AQA or software test engineering - Solid knowledge of SDLC, Agile methodologies, and QA/QE best practices Nice-to-Have Requirements: - Experience testing AI/ML systems - Familiarity with GenAI model evaluation metrics We offer a competitive salary range and opportunities for growth and development.