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.

Requirements

What do you bring to the table?

• 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.