Sushant Divate
Principal Software Engineer at Microsoft
Redmond, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolSushant Divate is a Principal Software Engineer based in Redmond with 7 years of professional experience building cloud-native and MLOps solutions at Microsoft after a decade-long engineering career that began at Harbinger Systems. He specializes in automating ML pipelines and deployment on Azure ML and Azure DevOps, contributing practical tooling like scoring-image scripts and pipeline step configurations to Microsoft’s MLOpsPython repository. Known for bridging infrastructure automation with model lifecycle concerns, he brings both hands-on engineering and delivery leadership to production ML workflows. His background in MCA and early full-stack engineering roles gives him strong foundations across systems, orchestration, and CI/CD that help teams reliably move models from experiment to production.
7 years of coding experience
13 years of employment as a software developer
Master of Computer Applications (MCA), Information Technology, Master of Computer Applications (MCA), Information Technology at Savitribai Phule Pune UniversityBachelor of Information Technology, Information Technology, Bachelor of Information Technology, Information Technology at University of Mumbai
English, Hindi, Marathi
Github Skills (10)
mlops10
python10
cicd10
azure-devops9
azure-devops-server9
docker8
mle8
ml8
dockers8
conda7
Programming languages (17)
Github contributions (5)
microsoft/MLOpsPython
Aug 2019 - Apr 2020
MLOps using Azure ML Services and Azure DevOps
Role in this project:
MLOps Engineer
MLOps EngineerContributions:30 commits, 27 PRs, 154 pushes in 8 months
Contributions summary:Sushant's commits primarily focus on building and modifying MLOps pipelines within Azure ML. Contributions include defining and configuring pipeline steps for model training, evaluation, and registration. The user also created a script for creating scoring images for deployment and made adjustments to existing components, such as environment management and deployment configurations, showcasing experience with infrastructure automation within the context of MLOps.
devopsazure-mlmlopsazure-machine-learningmachine-learning
sudivate/contoso-chat
Sep 2024 - Nov 2024
This sample has the full End2End process of creating RAG application with Prompt Flow and AI Studio. It includes GPT 3.5 Turbo LLM application code, evaluations, deployment automation with AZD CLI, GitHub actions for evaluation and deployment and intent mapping for multiple LLM task mapping.
Contributions:4 reviews, 22 PRs, 67 pushes in 2 months
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