Kirit Thadaka
Product Manager Synthetic Data
Seattle, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolKirit Thadaka is a product leader with 11 years of experience building ML platforms and launching data products that move from research to production. Currently leading Synthetic Data at NVIDIA after guiding Gretel’s Data Designer to market as the world’s first compound AI system for high-quality synthetic datasets, he combines product strategy with deep technical chops. At AWS he scaled SageMaker experimentation tooling from concept to a six-figure MRR product and led the high-participation MLflow beta, evidencing strong customer validation and go-to-market execution. His background spans hands-on MLOps, backend engineering, and applied ML—contributing to projects like Jupyter and SageMaker examples where he improved file management testing and CI/CD integrations for model deployment. A mentor and advisor to startups focused on sustainable futures, he also teaches and writes about ML, and balances technical rigor with creative pursuits like furniture making and electronic music. Based in Seattle, he brings a rare mix of product leadership, implementation expertise, and practical open-source contributions that accelerate ML teams.
10 years of coding experience
8 years of employment as a software developer
Bachelor of Technology (B.Tech.) Computer Engineering, Bachelor of Technology (B.Tech.) Computer Engineering at COEP Technological University
Hindi, French
Stackoverflow
77reputation
5kreached
2answers
7questions
Github Skills (30)
file-management10
python10
jupyter10
testing10
machine-learning10
cicd10
sagemaker10
gitlab10
automation10
deeplearning-ai10
mlops10
deep-learning10
lambdas10
aws10
lambda10
Programming languages (9)
Github contributions (5)
aws-samples/sagemaker-custom-project-templates
Aug 2021 - May 2022
Role in this project:
MLOps Engineer
MLOps EngineerContributions:7 reviews, 16 commits, 27 PRs in 9 months
Contributions summary:Kirit focused on modifying and enhancing GitLab integration components within the SageMaker project templates. Their contributions involved updating Lambda functions, specifically related to project creation, triggering pipelines, and deploying models within the GitLab environment. They addressed project search keys, group ID handling, and other GitLab API interactions, reflecting expertise in automating machine learning workflows with CI/CD principles. Their work appears to improve the integration and automation of model deployment processes.
aws/amazon-sagemaker-examples
Jul 2021 - Dec 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:13 reviews, 11 commits, 22 PRs in 1 year 5 months
Contributions summary:Kirit contributed significantly to example notebooks within the AWS SageMaker examples repository, focusing on building, training, and deploying machine learning models. The user implemented and updated notebooks demonstrating SageMaker pipelines, including steps for hyperparameter tuning, model evaluation, model registration, and conditional model deployment using Lambda functions. Their work showcases practical applications of SageMaker features for end-to-end machine learning workflows, including the latest updates to the SageMaker SDK.
pythonjupyter-notebooktrainingawssagemaker
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