FIND_THE_RIGHTJOB.
Sr Advanced Software Engr
India
P
H
- roficient in Python programming. S
- trong understanding of machine learning concepts and principles. A
- bility to articulate and document test cases for analytic machine learning algorithms based on project requirements. C
- onduct manual testing for analytics algorithms to ensure functionality and performance. A
- bility to design the synthetic data for testing the algorithms and models S
- imulate real time data set and find the breaking point or weakness in the algorithm / models S
- trong knowledge in testing the models and providing recommendations K
- nowledgeable in data analytics and data engineering processes that facilitate data preparation for testing phases. S
- olid understanding of key performance metrics and evaluation criteria relevant to machine learning testing. E
- xperience in conducting functional, integration, and regression testing to verify the accuracy and robustness of machine learning models. C
- apable of preparing comprehensive test reports, including accuracy analysis and user interface testing results. D
- emonstrated skills in evaluating machine learning models and developing effective test cases for model assessment. D
- esign and implement detailed testing strategies for machine learning and generative AI applications, ensuring high standards of quality and performance. E
- mploy logical reasoning to analyze the strengths and weaknesses of various testing solutions and methodologies in the context of AI systems. D
- evelop and implement automated testing frameworks for AI models using programming languages, including Python and C#.Net. C
- ollaborate effectively with cross-functional teams, including data scientists and software developers, to define testing requirements and ensure alignment with expected outcomes of AI features. E
- xperience working with substantial datasets stored in relational and document databases, such as PostgreSQL and MongoDB, to validate data integrity and operational performance. E
- nsure the reliability and robustness of AI solutions through rigorous unit and integration testing, with a focus on edge cases and usability assessments.
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