Artificial Intelligence Assurance Specialist
1 day ago
Redefining the future of travel technology through our experiential marketplace and SaaS platform, leveraging digital transformation, data science, machine learning, and more.
Estimated Salary: $120,000 - $180,000 per yearJob DescriptionAI Quality Assurance Engineer Role OverviewWe are seeking an AI Quality Assurance Engineer to play a critical role in designing and implementing innovative AI solutions that meet the unique needs of our clients and ensure seamless adoption of our technology.
- AI System Testing
- Develop and execute test plans and cases for AI/ML models, algorithms, and applications.
- Validate model outputs for accuracy, fairness, and reliability across use cases.
- Test AI system integration with enterprise platforms and applications.
- Model Validation
- Perform regression testing, Performance testing, stress testing, and scenario-based validation for models.
- Ensure models adhere to performance metrics (e.g., precision, recall, F1-score).
- Monitor and evaluate model drift and retraining requirements post-deployment.
- Automation and Tools
- Design and implement automated testing frameworks for AI systems.
- Leverage testing tools such as PyTest, Selenium, or specialized AI testing platforms.
- Data Quality Assurance
- Verify the integrity, accuracy, and consistency of training and testing datasets.
- Implement data validation frameworks to identify and rectify data quality issues.
- Compliance and Ethics Testing
- Audit AI systems for compliance with ethical guidelines and regulatory standards.
- Ensure adherence to fairness, bias mitigation, and explainability standards.
- Documentation and Reporting
- Maintain detailed documentation of test cases, results, and issue resolutions.
- Report QA metrics, such as defect density, test coverage, system performance, and defect escape rate.
- Experience with QA methodologies and tools such as JUnit, Postman, and PyTest.
- Proficiency in automation testing and CI/CD pipelines for AI systems.
- Knowledge of performance testing, model evaluation metrics, and experience with AI/ML models, data pipelines, and ML frameworks like TensorFlow, PyTorch, and Scikit-learn.
- Understanding of database technologies, including MongoDB, and big data tools such as Spark and Hadoop, with basic exposure to REST API testing.
- Ability to diagnose and resolve performance issues, with programming skills in Java, JavaScript, or similar languages being beneficial.
- Familiarity with ethical AI practices and regulatory compliance standards like GDPR and CCPA, and experience with cloud platforms such as GCP or AWS is preferred.
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