Role Overview:
As an SDET II, you will play a critical role in ensuring the quality and reliability of software in our AI-focused teams. You will collaborate closely with developers, data scientists, and other stakeholders to understand AI/Data Science requirements, design test strategies, and automate testing to validate AI models, APIs, and data pipelines. Your expertise in automation, API testing, and validation of AI/ML solutions will be pivotal in delivering high-quality AI-driven products.
Key Requirements:
🔹 AI/ML Testing Expertise
- Experience with testing AI/ML models, including model validation, accuracy testing, and output consistency.
- Familiarity with AI/ML libraries and frameworks like TensorFlow, PyTorch, or scikit-learn is a plus.
- Ability to design tests for data preprocessing pipelines and model performance metrics such as accuracy, precision, recall, and F1 score.
🔹 Hands-On Automation Experience
- Proven experience in building and maintaining automation frameworks (e.g., Selenium, Rest Assured, Cypress).
- Strong knowledge of scripting in Python, Java, or C# with an ability to write clean, efficient, and reusable test scripts.
🔹 API Testing & Automation
- Expertise in API automation using tools like Postman, Rest Assured, or equivalent.
- Ability to perform API chaining, create end-to-end test scenarios, and validate response bodies with complex assertions.
🔹 CI/CD Pipeline Integration
- Proficiency in integrating automation tests within CI/CD pipelines using tools like Jenkins, Bitbucket, or Azure DevOps.
- Experience with pipeline configuration using YAML or similar methods.
🔹 BDD Frameworks (Behavior-Driven Development)
- Hands-on experience with BDD tools such as Cucumber or JBehave.
- Ability to define scenario outlines, implement step definitions, and manage test data for comprehensive test coverage.
🔹 Data Validation & Troubleshooting
- Experience with testing data integrity, transformations, and ETL pipelines.
- Strong debugging skills to identify and resolve test failures, including handling edge cases in AI models and pipelines.
🔹 Agile and Test Strategy
- Ability to define risk-based and regression test strategies for AI-driven solutions.
- Strong manual and exploratory testing skills for ensuring robustness in new AI features and functionalities.
