Vineet Khare
Principal Researcher at Partify AI
Greater Seattle Area United States
Summary
🤩
Rockstar🎓
Top SchoolVineet Khare is a principal researcher and seasoned AI leader with over a decade driving research and engineering across Amazon, Meta, Microsoft, and startups. He has led cross-functional teams to scale deep learning platforms and search science—shipping production ML systems from Alexa’s petabyte-scale training to Amazon.com search experimentation and optimization. A PhD in Computer Science, he blends foundational research (neural networks, evolutionary algorithms) with practical cloud ML work, including contributed SageMaker XGBoost example notebooks that demystify distributed training and deployment. As a founder of Partify AI he pairs product-minded entrepreneurship with hands-on model building, and currently focuses on foundation LLMs for cybersecurity at Microsoft. Notably, he moves fluidly between research, platform engineering, and product metrics—accelerating experiments into measurable customer impact.
8 years of coding experience
15 years of employment as a software developer
Indian Institute of Technology KanpurPhD Computer Science, PhD Computer Science at University of Birmingham
Hindi, German
Github Skills (10)
xgboost10
machine-learning10
jupyter-notebook10
amazon-sagemaker10
aws10
python9
deep-learning8
inference8
data-science8
trainings7
Programming languages (2)
Github contributions (5)
aws/amazon-sagemaker-examples
Nov 2017 - Apr 2019
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:43 commits, 31 PRs, 26 pushes in 1 year 5 months
Contributions summary:Vineet primarily focused on adapting and demonstrating the use of the SageMaker XGBoost algorithm for various machine learning tasks within the AWS SageMaker ecosystem. Their contributions included creating and adapting example notebooks to showcase single-machine and distributed training workflows for multiclass classification and regression problems using the XGBoost algorithm. The user also integrated the model with the SageMaker hosting services, demonstrating the end-to-end process from data ingestion and preprocessing, training, model deployment, and validation. The work involved the creation of new directories for different use cases.
pythonjupyter-notebooktrainingawssagemaker
vrkhare/streamlit-venue-search
Sep 2023 - Oct 2023
Contributions:20 pushes in 1 month
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.