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Automation Testing + Python
1 week ago
Test Lead ( 9 to 12 Years)
Responsibilities:
Develop, implement, and oversee detailed test strategies for AI applications, covering all aspects of functional, performance, and regression testing.
Lead and mentor a testing team, driving best practices and optimizing testing processes.
Apply GenAI tools specifically designed for testing, including experience with large language models (LLMs) and ETL/data validation.
Senior Specialist ( 3 to 4 Years)
Required Skills:
Strong experience with testing tools and frameworks for automation across various platforms.
Proficiency in coding and scripting languages, specifically Python and Java, for effective automation.
Hands-on experience with GenAI tools for testing, testing of large language models (LLMs), and ETL/data testing.
Expertise in creating Excel macros for enhanced test reporting and data handling.
Key Responsibilities:
Leverage expert knowledge of QA methodologies to ensure comprehensive testing coverage and quality assurance.
Conduct UI and back-end testing, implementing automation best practices across multiple testing stages.
Utilize industry-standard testing and bug-tracking tools to efficiently manage testing workflows and defect tracking.
Write and maintain automated test scripts using programming languages such as Java, C#, Python, or JavaScript.
Test Specialist ( 6 to 7 Years)
Required Skills:
In-depth understanding of QA methodologies and hands-on experience with popular testing and bug-tracking tools.
Strong experience in software testing, with a focus on developing and executing automated test scripts.
Proficiency in Java, C#, Python, or JavaScript for effective automation of test cases.
Basic scripting skills (e.g., using Python), Automation framework development, familiarity with testing frameworks and tools (e.g., Selenium, JUnit, TestNG, Cucumber).
Proficiency in programming languages such as Python, R, or Java for test automation and data manipulation. Cloud and Virtualization Technologies
Ability to design, develop, and implement automated test cases for complex data pipelines.
Experience with data validation and data preprocessing techniques. Proficiency in programming languages such as Python, R, or Java for test automation and data manipulation.
Proven experience in software testing, with a focus on AI or machine learning applications preferred. Strong knowledge of machine learning concepts, algorithms, and frameworks (e.g., TensorFlow, PyTorch). Familiarity with cloud platforms (e.g., AWS, Azure) and tools for deploying AI models.
Create detailed test cases based on requirements and specifications, focusing on AI-specific aspects such as model accuracy, bias detection, and data integrity. Perform manual and automated testing of AI models and systems to ensure they meet performance and quality standards. Assess AI models for performance metrics such as accuracy, precision, recall, and F1 score.