Ashwin Ramesh
Machine Learning Engineer at Adobe
San Jose, California, United States
Summary
🤩
Rockstar🎓
Top SchoolAshwin Ramesh is a Machine Learning Engineer with nine years of experience who combines academic research in ML and neuroscience with production-focused systems engineering. Currently pursuing a master's while building ML infrastructure at Continual, he previously worked on deep-learning systems at NVIDIA and gained broad big-data and distributed-systems experience through internships and teaching roles at UIUC. He is an active contributor to production ML tooling, including improving CI/CD and adding GCS/S3 cloud storage support to NVIDIA’s Triton Inference Server, showing a practical talent for shipping model-serving features at scale. Based in Morgan Hill, CA, he specializes in large-scale distributed platforms and cloud-native integrations that bridge research prototypes and reliable production deployments.
10 years of coding experience
4 years of employment as a software developer
CBSE - 12th Grade, PCMC, 97.2%, CBSE - 12th Grade, PCMC, 97.2% at CMR National Public SchoolICSE - 10th Grade, 94.2%, ICSE - 10th Grade, 94.2% at New Horizon Public SchoolMaster of Science - MS, Computer Science, 4.0, Master of Science - MS, Computer Science, 4.0 at University of RochesterBachelor of Technology, Mechanical Engineering, Bachelor of Technology, Mechanical Engineering at National Institute of Technology Karnataka
English, Tamil, Hindi
Github Skills (20)
amazons310
cloud-computing10
inference10
gcp10
amazon-s310
ci-cd10
cloud-storage10
aws-s310
s3bucket10
devops10
testing9
dockerce9
dockers9
docker9
aws9
Programming languages (2)
Github contributions (5)
triton-inference-server/server
Jun 2019 - Aug 2019
The Triton Inference Server provides an optimized cloud and edge inferencing solution.
Role in this project:
DevOps Engineer & Cloud Engineer
DevOps Engineer & Cloud EngineerContributions:11 reviews, 10 commits, 29 PRs in 1 month
Contributions summary:Ashwin's contributions primarily revolve around enhancing the Continuous Integration and Continuous Deployment (CI/CD) pipeline and integrating cloud storage functionalities within the Triton Inference Server project. This includes creating CI jobs to test cloud storage implementations, specifically for Google Cloud Storage (GCS) and S3. The user addressed build issues, implemented cloud storage features, and modified the testing framework to accommodate cloud storage integration. The modifications include adding support for S3, fixing bugs related to GCS, and updating the CI test to include bucket testing.
nvidia-dockernvidiadeep-learninggpuinference
xprotobeast2/shwinOS
Feb 2016 - Dec 2017
Contributions:1 push, 2 branches in 1 year 10 months
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