Sunil Surineni
About Sunil Surineni
Sunil Surineni is a Staff Machine Learning Engineer at Blaize, specializing in neural network optimization and the deployment of AI models on embedded platforms. He has expertise in generative applications, generative adversarial networks, and reinforcement learning.
Work at Blaize
Sunil Surineni has been employed at Blaize since 2014, where he currently holds the position of Staff Machine Learning Engineer. In this role, he focuses on neural network optimization to enhance the efficiency of AI model deployment. His work involves the development of generative applications and performing inference on edge devices. Prior to his current role, he served as a Research Associate at Blaize from 2014 to 2017, contributing to various projects in the field of machine learning.
Education and Expertise
Sunil Surineni completed his undergraduate studies at Kakatiya University from 2009 to 2013. He then pursued a Master of Technology (MTech) in automotive electronics at Vellore Institute of Technology, graduating in 2015. His academic background has equipped him with expertise in generative adversarial networks and reinforcement learning, which he applies in his current role at Blaize.
Background
Sunil Surineni has a strong foundation in machine learning and AI, with a specific focus on the deployment of AI models on embedded platforms and low power devices. His experience spans over a decade, with a significant portion of his career dedicated to optimizing neural networks for efficient AI applications. He is based in the Hyderabad Area, India, where he has worked on-site throughout his tenure at Blaize.
Specialization in AI and Machine Learning
Sunil specializes in the optimization of neural networks, which is critical for the effective deployment of AI models. His work includes the development of generative applications that leverage advanced machine learning techniques. He also focuses on inference processes on edge devices, ensuring that AI solutions are efficient and effective in real-world applications.