QA Engineer - AI

1. AI Model and Pipeline Validation

· Design and implement test plans and test cases for AI frameworks to ensure comprehensive validation of functionality, accuracy, and performance.

· Validate AI model outputs, identify discrepancies, and report bias in results.

· Ensure end-to-end validation of data pipelines, including data ingestion, preprocessing, transformation, and model deployment.

· Identify and resolve functional and non-functional issues in AI models and data workflows.

2. Test Automation & Scripting

· Develop and execute automated test scripts using Python.

· Utilize test automation tools like Selenium, PyTest, or similar frameworks to enhance testing efficiency.

· Implement CI/CD testing frameworks for AI/ML pipelines.

3. Cloud-Based AI Testing

· Conduct AI application testing on cloud platforms such as AWS, Azure, and Google Cloud.

· Ensure AI model performance remains optimal across different environments and scenarios.

· Validate large datasets, ensuring data preprocessing, augmentation, and quality assurance.

4. AI/ML Model Performance Testing

· Identify and report bottlenecks in AI models.

· Conduct stress testing for AI models in real-world use cases.

5. Collaboration & Quality Assurance

· Work closely with AI/ML engineers, data scientists, and software engineers to define quality standards.

· Contribute to compliance with AI-related regulatory and industry best practices.

· Document and maintain test cases, defect reports, and validation procedures for AI systems.

· Strong experience in designing and implementing test cases for AI frameworks.

· Experience in validating AI model outputs and identifying discrepancies or biases.

· Attention to detail in identifying functional and non-functional issues in AI models.

· Proficiency in scripting and developing automated test scripts using Python

· Hands-on experience with test automation tools (Selenium, PyTest, etc.).

· Experience with cloud-based testing platforms (AWS, Azure, Google Cloud).

Preferred Skills:

· Familiarity with validating large datasets and ensuring data quality.

· Knowledge of AI/ML model evaluation tools like TensorFlow, Keras, PyTorch.

· Experience in performance testing of AI models in diverse environments.