Working for BMW Group, MunichAnalyzed and validated requirements for comprehensive unit testing and regression testing of automotive ECU for Battery Management Systems.Executed unit tests and regression tests on C language source code, ensuring compliance with ASIL B and ASIL C standards.Adhered to the V model for structured and systematic software testing processes.Utilized Tessy to develop and execute detailed test cases, ensuring thorough coverage and accuracy.Conducted functional reliability and compatibility testing to ensure robust performance across various scenarios.Executing SIL tests on the test benches to Verify and improve branch and statement coverage.Worked for Zeiss Meditech, MunichOct 2022- Dec 2023-Developed and customized Linux environments using Yocto for medical technology applications.-Customized kernel and OS by writing and maintaining Yocto recipes.-Created and maintained BSPs (Board Support Packages) to support new hardware platforms.-Proficient in using BitBake, Poky, and Buildroot for efficient build management and system customization.-Utilized QEMU for hardware emulation and testing of embedded systems.-Customized kernel and OS by writing and maintaining Yocto recipes.-Managed and optimized CI/CD pipelines using Azure DevOps to facilitate seamless integration and deployment processes.

-Executed data cleaning, data balancing and data manipulation-Developed a multi-class text classification model using Python-Built a Word2Vec model from scratch using Gensim python library-Worked in Agile development process-Source Code Management (SCM), Continuous Integration

Vasodilation detection (Human Skin Redness) based on Image Processing and Machine Learning-Implemented different CNN architectures like a sequence ofConvolutions, Pooling, and Activation functions to improve the accuracy.-Designed a data pipeline to make it easy to do feature engineering & build a ML model-Worked with the research team to train a model on the GPU node of with state of the art U-net architecture to make the great output of the above research topic-Performed the image segmentation to train the ML model-Achieved an accuracy of 94% in skin redness detection

1.Design and develop interface automation to reduce / eliminate manual handling of parts2.Make systematic improvements to enhance existing capabilities and technologies3.Help to reduce equipment breakdowns and ensure a quality product is being delivered4.Worked on Spot Quality Improvement (by adjusting the current, holding time, force and angle of robot)5.Worked on Proximity, RTD, PIR, Flow Sensors and actuators6.Ensure the latest developments in manufacturing technology are implemented through the development phases using the manufacturing readiness level processes