About

Shashank R. is an analytics aficionado with over 4 years of experience in the field, working with various analytics and data ecosystem progenitors such as Google TensorFlow, Rigetti Computing, and TIME magazine. He is a hard-headed problem solver who focuses on discerning the merits and demerits of Deep Learning algorithms and the optimization of their architectures to engineer complete data-driven and polymorphic-modular Deep Learning systems. Currently, he leads a team of 9 at TensorFlow Extended (TFX) and consults with prospective startups intending to harness AI for solving real-world problems by establishing pragmatic solutions. As an Analytics Consultant at Rigetti Computing, Shashank built and delivered integrated superconducting quantum systems over the cloud and developed software solutions optimized for hybrid quantum-classical computing. He worked remotely to develop specialized Machine Learning Autoencoder algorithms to control the microwave components used in cryogenic environments for scalable quantum computing systems. He also formulated, deployed, and tested three different Deep Learning Q-learning super algorithms to optimize backpropagation within superconducting quantum computers with more than 30 qubits. Previously, Shashank worked as a Software Engineer (Machine Learning) at Meredith, where he implemented Machine Learning projects to optimize the General Ledger (GL) String process managed by the India Accounting team. He also worked on a user profiling tool with the Big Data team and underwent training for Java, Amazon AWS, Bash scripting, and Amazon Redshift. Shashank has worked with two different teams at Google, including TensorFlow Extended (TFX), where he spearheaded the effort to come up with better Deep Learning architecture models to compete with the ecosystem that makes end-to-end ML pipeline possible. He led a team of 9 to transform, serve, and analyze the Metadata and real-time data and establish Neural Structured Learning within polymorphic data. He also put his effort into vigilante all the executables which are usually transformed by TensorFlow when large-scaling POCs to industry scale models and architectures. Shashank holds a Bachelor's degree in Engineering from RV College of Engineering and has expertise in ML, Deep Learning, TensorFlow, AI, Spark, Python, Neural Networks, AWS, Software Engineering, NLP, Scikit, Big Data, and Java. With over 7 years of relevant experience, Shashank is a skilled professional who is passionate about Deep Learning & Analytics, Venture Capital & Startups, and even Rock, Blues & Jazz electric guitar.