Ashwin Srinivasan is a Data & Applied Scientist at Microsoft with a focus on NLP and productionizing state-of-the-art language models, building on an MS from Carnegie Mellon (2019) and four years of professional experience. He transitioned from software engineering into applied research, combining practical backend and DevOps skills—evident from contributions to the SONiC open-source projects where he improved build infrastructure, logging, and robustness. At Microsoft he has shipped enterprise-scale Turing language model work while drawing on prior software engineering and data science internships to bridge research and production. Based in Redmond, he brings a pragmatic engineering mindset to model deployment and platform reliability, often solving subtle operational issues that improve long-running systems.
4 years of coding experience
3 years of employment as a software developer
Bachelor of Technology - BTech, Bachelor of Technology - BTech at Manipal Institute of Technology
Master of Science in Inteligent Information Systems, Master of Science in Inteligent Information Systems at Carnegie Mellon University
Contributions:26 reviews, 19 commits, 40 PRs in 11 months
Contributions summary:Ashwin primarily focused on enhancing the stability and robustness of the SONiC configuration management system. Their contributions include fixing a critical bug in the reboot history queue, ensuring correct synchronization between the DUT and internal queues, and adding support for handling "Unknown" and "Thermal Overload" reboot causes. The user also implemented code to address older SONiC images and platform-specific configurations. Further contributions include addressing an issue related to the absence of a specific configuration file and correcting errors in PSU status checks.
Scripts which perform an installable binary image build for SONiC
Role in this project:
DevOps Engineer & Backend Developer
Contributions:30 reviews, 2 commits, 39 PRs in 7 months
Contributions summary:Ashwin primarily contributed to the infrastructure and build process, making improvements to Dockerfiles, build scripts, and dependency management. They addressed Python 2 dependencies, added necessary packages (e.g., `libpci`, `blkinfo`, `psutil`) to Docker images and the host environment, and implemented code optimizations to reduce disk writes. Furthermore, the user enhanced system logging and configuration management, including fixes to prevent broken pipe issues in the logrotate script.
armv6installableimage-buildsonicbinary-image
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