Raghu Ramesha
Manager, Solutions Engineering And Architecture - Generative AI
San Francisco, California, United States
Summary
👤
Senior🎓
Top SchoolRaghu Ramesha is a seasoned engineering leader with 9 years of experience building and scaling AI and cloud-native systems, now managing Solutions Engineering and Architecture for Generative AI at NVIDIA in San Francisco. He previously led GenAI and SageMaker/Bedrock platform engagements at AWS, architecting production LLM and ML solutions for Fortune 500 customers and influencing over $100M in revenue while spending the majority of his time coding and building POCs. His background spans real-time media platforms at Intel—where he architected cost-saving, Kubernetes-based video pipelines used in global live events—and earlier roles in network modernization and aerospace software. An active practitioner of applied GenAI, he contributes practical examples to popular repos like Amazon SageMaker examples, including asynchronous inference walkthroughs and end-to-end diagrams that help teams deploy at scale. Colleagues describe him as a technical mentor who pairs deep hands-on engineering with product-minded influence across customers and core service teams.
9 years of coding experience
7 years of employment as a software developer
Master’s Degree Computer Science Data Science and Machine Learning, Master’s Degree Computer Science Data Science and Machine Learning at The University of Texas at DallasBachelor of Engineering (BE) Computer Science, Bachelor of Engineering (BE) Computer Science at Ramaiah Institute Of Technology
English, Hindi, Kannada, Tamil
Stackoverflow
11reputation
13kreached
0answers
2questions
Github Skills (16)
amazon-sagemaker10
jupyter-notebook10
machine-learning10
deep-learning10
aws10
inference10
data-science9
python9
aws-s38
s3-bucket8
amazon-s38
lightning6
appium6
selenium6
android6
Programming languages (5)
Github contributions (5)
aws/amazon-sagemaker-examples
Aug 2021 - Oct 2022
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
Role in this project:
ML Engineer
ML EngineerContributions:24 reviews, 5 commits, 9 PRs in 1 year 2 months
Contributions summary:Raghu implemented and updated Jupyter notebooks to demonstrate and test Amazon SageMaker Asynchronous Inference. The commits include adding a new notebook, `Async-Inference-Walkthrough.ipynb`, which covers the creation and usage of asynchronous inference endpoints and the associated steps. The contributions involved modifying code examples to reflect updated IAM policies and ensure correct functionality. The user also contributed to the example by adding end-to-end diagrams and dataset information.
pythonjupyter-notebooktrainingawssagemaker
Contributions:12 commits, 2 pushes in 6 months
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