QA Architect - R01571456

Job requirements

• Experience Range: With at least 7 years of quality assurance experience, including substantial hands-on work with data science and machine learning testing frameworks Key Responsibilities: Design and implement automated testing strategies for AI and data science outputs, ensuring accuracy and reliability across models and pipelines
• Develop and maintain robust evaluation and validation frameworks for backend and frontend components, leveraging statistical and machine learning techniques
• Collaborate with data scientists and engineers to define test cases, hypotheses, and statistical metrics for model assessment and improvement
• Integrate advanced statistical tests such as T-Test, Z-Test, and regression analyses into automated QA workflows to validate model performance
• Utilize tools like Great Expectations, Evidently AI, and specific machine learning frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet) to monitor, track, and report on model drift, anomalies, and forecast accuracy
• Optimize testing processes for scalability and efficiency using Python, PySpark, R, and related technologies in large-scale data environments
• Configure and manage testing infrastructure using platforms such as KubeFlow and BentoML to streamline deployment and evaluation cycles
• Troubleshoot and resolve issues in automated testing pipelines, driving continuous improvement and high-quality deliverables
• Required Skills: Advanced proficiency in Py