Nihal Harish
Senior Software Engineer at Robinhood
Palo Alto, California, United States
Summary
🤩
Rockstar🎓
Top SchoolNihal Harish is a Senior Software Engineer with 11 years of experience building ML infrastructure, AI agents, and backend systems across AWS and fintech. At Robinhood he focuses on agents and ML infra, after multi-year roles at AWS where he contributed to SageMaker Debugger and Personalize, shaping production recommender and model-debugging pipelines. He’s pragmatic and product-minded—“move fast, build things”—with hands-on MLOps experience updating deep learning container images, CUDA and framework builds, and integration tests for widely used AWS deep-learning tooling. Based in Palo Alto, he pairs an MS in Computer Science with a curiosity-driven open-source streak, helping ensure ML frameworks and tooling work reliably from development to deployment.
11 years of coding experience
6 years of employment as a software developer
Bachelor of Engineering (BE) Computer Science, Bachelor of Engineering (BE) Computer Science at PES UniversityMaster of Science - MS Computer Science, Master of Science - MS Computer Science at Stony Brook University
Stackoverflow
1,050reputation
120kreached
10answers
21questions
Github Skills (18)
pytorch10
docker10
dockers10
sagemaker10
mlops10
tensorflow10
aws10
build-automation10
cicd9
mxnet9
python8
testing8
dictionary6
virtualenv6
intergration6
Programming languages (8)
Github contributions (5)
aws/deep-learning-containers
Jun 2020 - Aug 2021
AWS Deep Learning Containers are pre-built Docker images that make it easier to run popular deep learning frameworks and tools on AWS.
Role in this project:
MLOps Engineer
MLOps EngineerContributions:23 reviews, 14 commits, 16 PRs in 1 year 3 months
Contributions summary:Nihal's primary contribution involves updating and maintaining the Deep Learning Container (DLC) images within the `aws/deep-learning-containers` repository. Their work centers around updating the `smdebug` library, a debugging tool specifically for AWS SageMaker, across various deep learning framework Dockerfiles (TensorFlow, PyTorch, MXNet). The changes also include updating TensorFlow binary URLs, upgrading CUDA versions, and adjusting tests related to the `smdebug` integration, showcasing a focus on build, test, and deployment aspects within the MLOps pipeline. The user contributed to ensuring the correct versions and configurations of the machine learning frameworks and supporting tools within the container images.
pytorchsagemakercontainersmxnetserving
awslabs/sagemaker-debugger
Aug 2019 - Feb 2022
Amazon SageMaker Debugger provides functionality to save tensors during training of machine learning jobs and analyze those tensors
Contributions:10 releases, 184 reviews, 871 commits in 2 years 6 months
sagemakeramazon-sagemakerdeep-learningamazonmachine-learning
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.