Machine Learning Tester - ETL/Datawarehouse Testing (3-6 yrs)

Requirements :

Expertise in Agile Methodology: Strong experience in Agile methodologies, with a focus on continuous testing and delivery cycles.

Python Programming: Proficiency in Python (preferred) and Java for AI and ML automation tasks.

Testing AI & ML Models: Experience testing Generative AI models, Large Language Models (LLMs), and AI-driven systems (e. g., Chatbots, AI agents).

Automation Frameworks for AI: Hands-on experience designing automation frameworks using tools like pytest, mlflow, Selenium, or Serenity BDD for AI/ML applications. Estimation techniques for Automation testing efforts (Design and Execution).

Cloud Automation Strategy: Ability to develop automation test strategies for AI/ML applications deployed on public and private cloud environments.

API and Web Services Testing: Strong experience in testing RESTful APIs and web services using tools like Postman and REST API.

CI/CD Automation: Experience in testing and test planning for CI/CD solutions, especially for AI-driven products.

Test Management Tools: Proficient in using Azure DevOps (ADO) or other test management tools to streamline test case management and reporting.

Various Testing Types: Experience in functional, regression, UI/usability, and integration testing for AI/ML applications.

Exposure to Cloud Platforms: Good exposure to cloud platforms such as AWS and Azure for deploying and testing AI systems.

AI Product Workflow Understanding: Familiarity with agentic workflows and AI system interactions in end-to-end product use cases.

Design and execute test cases for AI models, ensuring fairness, accuracy, and robustness.

- Evaluate AI outputs for bias, performance, and correctness using appropriate test strategies.

- Develop and implement automated test scripts for AI-driven applications. - Test Automation & Scripting

- Design and maintain automated test frameworks using Selenium, Playwright, or Cypress.

- Develop test scripts using Python, Java, or JavaScript for API and UI automation.

- Work with CI/CD pipelines to integrate automated testing into the software development lifecycle.

Functional & Performance Testing :

- Conduct end-to-end, regression, and performance testing for AI applications.

- Use testing tools such as JMeter, Postman, or LoadRunner to validate system performance.

- Identify, track, and report defects using JIRA, TestRail, or similar tools. - Collaboration & Quality Assurance

- Work closely with developers, data scientists, and DevOps teams to identify potential issues early.

- Ensure AI applications comply with best practices, industry standards, and ethical AI guidelines.

- Provide detailed reports and documentation for test strategies, plans, and results.