Bio:

Suvendu is a Senior Machine Learning Engineer on Amazon’s Devices Org, where he leads supervised fine‑tuning and RLHF pipelines for 7 B–470 B‑parameter LLMs. Over 14 years, he has designed large‑scale distributed‑training systems with Megatron‑LM 3‑D parallelism, DeepSpeed ZeRO, PyTorch FSDP, and AWS Trainium, consistently cutting training cost and latency without sacrificing model quality. His MLOps expertise spans SageMaker, MLflow, and on‑device TensorRT inference, driving 3× throughput and 30 % latency reductions for production workloads. Previously, Suvendu built high‑volume data lakes and real‑time recommendation engines at HBO Max and architected predictive‑maintenance ML platforms at Equinix. He is an active open‑source contributor—author of the MLOps framework on AWS’s GitHub—and a frequent mentor on distributed‑ML best practices. Suvendu holds a master’s degree in Computer Science, has presented at internal AWS tech talks, and enjoys demystifying cloud economics for ML practitioners.