Description

Define and implement testing strategies for AI/ML applications across cloud and edge environments.

Responsibilities

• Design and implement automated test frameworks for AI/ML pipelines (training, inference, monitoring).
• Validate model accuracy, robustness, drift, and bias across various datasets.
• Develop test suites for edge AI inference, cloud deployments, and hybrid pipelines.
• Conduct performance, latency, and throughput testing of AI inference on cloud (AWS, Azure, GCP) and edge devices.
• Define quality metrics for AI models (precision, recall, F1, robustness under adversarial attacks).

Required Skills

• 5–7 years in QA/Automation testing, with at least 2+ years in AI/ML systems testing.
• Strong background in test automation frameworks (PyTest, Robot Framework, Selenium, Playwright).
• Experience with API testing tools (Postman, REST Assured, Karate).
• Hands-on experience with performance testing tools (Locust, JMeter, k6).
• Proficiency in Python and Bash scripting.
• Experience with CI/CD tools including GitHub Actions, Jenkins, and GitLab CI.
• Familiarity with containerization technologies (Docker, Kubernetes).
• Knowledge of model evaluation metrics (ROC, AUC, F1-score, confusion matrix) and model validation tools (Evidently AI, WhyLabs).
• Experience testing AI applications in production across cloud and edge environments.

Key Skills

Education

Any Gradute