Summary
Aashish Sheshadri is a Machine Learning Architect with 12 years at PayPal, specializing in productionizing deep learning and ML-driven infrastructure across cloud, data, and developer platforms. He leads platform-level ML initiatives—autoscaling, autonomous remediation, capacity planning, and MLOps—for thousands of services, delivering measurable cost savings ( ~25% cloud reduction) while preserving five-nines availability. His work spans time series forecasting, RL-driven autoscalers, throughput optimization, and autonomous outlier detection with remediation, and he has a track record of enabling enterprise ML via Kubernetes-based notebooks and shared HPC access. Earlier research roles include inventing fast hashing primitives (FASH64/FASH256) and contributing to novel protocols and RNGs, reflecting a blend of applied research and systems engineering. Based in San Jose, he combines deep architectural thinking with hands-on implementation—occasionally bridging quantum relevance and NLP (BERT-era) defenses for information security.
13 years of coding experience
8 years of employment as a software developer
Master's Degree, Computer Science, Master's Degree, Computer Science at The University of Texas at Austin
Bachelor's Degree, Telecommunication / Electronics and Communication, Bachelor's Degree, Telecommunication / Electronics and Communication at Visvesvaraya Technological University
National Academy for Learning
Visiting Scholar, Computer Science, Visiting Scholar, Computer Science at Carnegie Mellon University
English, Kannada, Hindi, Telugu, Tamil