Summary
Rahul S is a Data Scientist with 9 years of experience building end-to-end machine learning systems, from data pipelines and model research to production inference. He has improved model performance and latency across domains—boosting F1 from 40% to 70% with a novel balancing algorithm and cutting inference time from 272 ms to 41 ms by deploying TorchServe microservices on Kubernetes. His background spans industrial deployments (embedded cross-compilation and ONNX adaptations for accelerators) to academic research (a CLIP + CenterNet few-shot detector that raised detection precision from 17% to 50%). Trained at TUM and IIT Madras, he combines strong engineering discipline with research rigor, having supported large-course labs and published dataset work. Based in Chennai, he focuses on practical, reproducible ML engineering and MLOps, often automating complex build and deployment pipelines for edge devices. An interesting detail: he routinely bridges hardware constraints and modern deep learning, e.g., automating multi-stage Docker cross-compiles for Jetson and TI SoCs to make models actually run in the field.
9 years of coding experience
4 years of employment as a software developer
Indian Institute of Technology Madras
Master of Science - MS Computational Science and Engineering, Master of Science - MS Computational Science and Engineering at Technical University of Munich
Bachelor of Technology - BTech Mechanical Engineering, Bachelor of Technology - BTech Mechanical Engineering at Shanmugha Arts, Science, Technology and Research Academy
English, Tamil, German