Description

Key Responsibilities:
Design and execute test strategies for AI/ML applications and data pipelines
Validate ML model outputs, accuracy, and performance metrics
Perform functional, integration, and regression testing for AI solutions
Test data pipelines, data quality, and preprocessing workflows used in ML systems
Conduct API testing for model inference and integration endpoints
Collaborate with Data Scientists, ML Engineers, and DevOps teams to understand model behavior and testing requirements
Perform model validation testing including drift, bias, and edge case scenarios
Participate in release validation, UAT support, and defect management
Develop automation scripts for testing AI/ML workflows (where applicable)
Prepare test plans, test cases, and QA documentation


Required Skills:
Strong experience in QA / Software Testing (functional & API testing)
Hands-on experience with testing tools such as JIRA, Rally, QTP, or similar
Experience in API testing tools (Postman/REST APIs)
Good understanding of SQL and database testing
Exposure to AI/ML fundamentals (model training, evaluation, prediction workflows)
Understanding of data validation, data quality, and ETL testing
Basic scripting knowledge (Python preferred)


Preferred Skills:
Experience working with Generative AI / LLM-based systems (prompt validation, output verification)
Knowledge of ML lifecycle (training, validation, deployment)
Exposure to data engineering tools (Spark, Airflow, Databricks)
Experience with automation frameworks (Selenium, PyTest, etc.)
Healthcare domain experience (payer systems – claims, enrollment, etc

Key Skills

Education

Bachelor's degree