Praneet Solanki
Senior Machine Learning Engineer at Microsoft
Cambridge, Massachusetts, United States
Summary
🤩
Rockstar🎓
Top SchoolPraneet Solanki is a Senior Machine Learning Engineer at Microsoft with nine years of experience building production-grade ML systems and MLOps pipelines. He specializes in fine-tuning and aligning large language models using PPO, DPO, LoRA/QLoRA, GRPO and implementing Retrieval-Augmented Generation, paired with robust evaluation frameworks for quality, safety, and performance. Based in Cambridge, MA, he blends deep engineering roots—from Big Data and analytics to scalable cloud deployments on Azure AKS—with applied data science rigor. His open-source contributions include automating Azure ML deployments in the widely-used microsoft/MLOpsPython repo, reflecting practical expertise in CI/CD and model serving. Passionate about autonomous agents, he focuses on systems that can reason, plan, and act reliably in real-world settings. Colleagues describe him as someone who bridges research techniques and production constraints to deliver resilient, safety-conscious ML products.
9 years of coding experience
4 years of employment as a software developer
Rajiv Gandhi Proudyogiki VishwavidyalayaMaster of Science Information Systems, Master of Science Information Systems at Northeastern UniversityGraduate Certificate Data Science, Graduate Certificate Data Science at Harvard Extension School
English
Github Skills (13)
continuous-deployment10
azure-aks10
azure-kubernetes-service10
mlops10
azure-machine-learning10
python10
ml-deployment10
dockers9
cicd9
azure-devops9
azure-devops-server9
docker9
machine-learning8
Programming languages (8)
Github contributions (5)
microsoft/MLOpsPython
Feb 2019 - Jul 2019
MLOps using Azure ML Services and Azure DevOps
Role in this project:
MLOps Engineer
MLOps EngineerContributions:90 commits, 7 PRs, 16 pushes in 4 months
Contributions summary:Praneet's commits primarily focus on automating the deployment and management of machine learning models using Azure Machine Learning (Azure ML) services. They are involved in creating and updating Azure Kubernetes Service (AKS) webservices, managing model registration, and configuring scoring images. The code changes indicate expertise in integrating models with Azure DevOps pipelines and implementing MLOps practices for model deployment and testing. The user also demonstrates knowledge of Python scripting for deploying ML models and managing associated configurations.
devopsazure-mlmlopsazure-machine-learningmachine-learning
Azure/cortana-intelligence-energy-supply-optimization
Apr 2017 - Jul 2017
Resource Optimization
Contributions:62 commits in 3 months
optimizationresource-optimization
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