About

AJAY CHANDRA NALLANI is a Deep Learning Engineer at Aptiv with over 2 years of experience in developing applications based on computer vision and machine learning. He is a graduate from Purdue University with majors in Computer Information Technology. AJAY has expertise in Computer Vision, Deep Learning, Computer Networks, Cloud Computing, and Hardware Accelerators. AJAY is a quick learner and is able to comply with different development processes. He has experience in time-critical projects and has managed sensitive materials while providing after-hours support for clients. His colleagues know him as a problem solver who can work well alone, but is at his best collaborating with others. AJAY is proficient in Python and C++ programming languages and has experience working with computing platforms such as CUDA and OpenCL. AJAY has worked with various frameworks such as Tensorflow and PyTorch and has experience in using Git, Linux, Shell scripting, Microsoft Azure, Jupyter Notebooks, Eclipse, and Matlab. He is well-versed in Agile methodologies such as Scrum and Kanban and has experience working with libraries such as Keras, Pandas, SciPy, NumPy, re, Scikit Learn, Matplotlib, and OpenCV. AJAY has also worked with serial protocols such as SPI, I2C, UART, and USB and networking protocols such as UDP, TCP, HTTP, and MQTT. Prior to his current role, AJAY worked as a Software Developer at Aptiv and as a Project Engineer Intern at Siemens. He holds a Master's degree in Computer/Information Technology from Purdue University and a Bachelor of Technology degree in Electronics and Communications Engineering from SR Engineering College. In summary, AJAY CHANDRA NALLANI is a highly skilled Deep Learning Engineer with relevant experience of over 6 years. He is proficient in various programming languages, computing platforms, frameworks, technologies, methodologies, and libraries. AJAY is a quick learner, problem solver, and a collaborative team player who has experience working on time-critical projects and managing sensitive materials.