No Sponsorship Available
Must be a USC or GC or H4/L2-EAD
Duration - 6+ months
Location - Irving, TX - 3 days onsite
Qualifications
- 5+ years QA experience, 1+ year QA ML experience
- Proven experience in testing and validating machine learning models, including unit tests, integration tests, and performance tests for ML models.
- Familiarity with the specific challenges of testing in ML, such as model accuracy, bias detection, and data quality validation.
- Strong understanding of machine learning algorithms, model evaluation metrics, and the ML lifecycle.
- Experience with common ML frameworks like TensorFlow, PyTorch, Scikit-learn, or similar.
- Experience with automating tests for ML models and data pipelines, using tools like pytest, TensorFlow Extended (TFX), or custom testing frameworks.
- Proficiency in scripting languages such as Python, for developing automated tests and integrating them into CI/CD pipelines.
- Hands-on experience in ensuring data quality, including data preprocessing validation, feature engineering checks, and monitoring for data drift.
- Familiarity with tools like Great Expectations, Deequ, or similar for automating data validation.
- Experience integrating testing practices into CI/CD pipelines (Azure DevOps, Azure Pipelines), particularly in ML environments.
- Familiarity with containerization (Docker) and orchestration (Kubernetes) for deploying and testing ML models in various environments.
- Ability to work closely with ML engineers to develop testing strategies that align with the specific needs of ML projects, including model robustness, fairness, and compliance.
