Job details

SDET II - Automation for AI/ML

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.

3+ years of experience in software testing, with a focus on automation and QA for AI/Data Science applications.
Strong knowledge of testing frameworks and methodologies.
Proficiency in API, ETL, or AI/ML testing.
Familiarity with CI/CD processes and tools (e.g., Jenkins, Azure DevOps).
Experience with AI/ML model validation and related testing challenges is a plus.
Excellent problem-solving skills and attention to detail.