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    Gen AI Automation Tester - Bengaluru, India - PwC India

    PwC India
    PwC India Bengaluru, India

    1 week ago

    Default job background
    Description

    Location:Bangalore

    Experience Required: 4 to 7 years

    Job Description:


    • Design, develop, and maintain automated test frameworks and scripts for chat interface applications utilizing LLMs, ensuring comprehensive coverage of all functionalities and user scenarios.


    • Understand Retrieval Augmented Generation(RAG) strategy to improve testing for knowledge-intensive tasks by retrieving relevant documents or data that can be used as references or benchmarks for evaluating the LLM's responses


    • Evaluate chat interactions for adherence to conversational design principles, ensuring responses are accurate, relevant, and delivered within acceptable time frames.


    • Perform thorough regression testing when bugs are resolved and when there are updates to LLMs or application features.


    • Collaborate with development teams to understand application features and integration points for Large Language Models, identifying potential areas for automation.


    • Execute automated and manual test cases, analyze test results, identify and document defects, and work closely with developers to ensure they are addressed promptly.


    • Monitor the performance of LLMs in the chat interface, identifying any issues with latency, accuracy, or relevance of responses.


    • Stay up-to-date with the latest developments in AI, machine learning, and chatbot technologies to continually improve testing strategies and tools.


    • Collaborate with product managers and UX designers to ensure the chat application meets the highest standards of user satisfaction and engagement.


    • Ensure compliance with data privacy and security policies during the testing process, especially when handling sensitive user data.


    • Provide detailed reports and documentation on test activities, results, insights, and recommendations for improving the application's quality and performance.

    Tools Exposure Expected:


    • Automation Testing: Selenium, Appium, TestComplete.


    • Programming Languages: Python, JavaScript, or Java, which are commonly used in test script

    development.


    • API Testing: e.g., Postman, REST-Assured) for testing chat application interfaces and integrations.


    • Machine Learning Models: For how LLM function


    • Test Case Design: conversational flows, edge cases, and user intents in chat applications.


    • Bias and Fairness: Awareness of potential biases in AI models


    • Security Testing of chat interface

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