Open to exciting opportunities

Karan
Chaudhari

Software Engineer 2 at Microsoft · Azure Storage & AI Infrastructure

Building systems that scale globally — from optimizing cloud storage contributing to $1B+ in Azure Files revenue to leading AI infrastructure for enterprise workloads.

GitHub LinkedIn 🌐 krnch.me
Jersey City, NJ
About

Building at scale,
shipping with impact.

I'm a software engineer with 8+ years of experience building high-performance distributed systems across cloud storage, financial risk platforms, and AI infrastructure.

At Microsoft, I work on Azure Files — improving storage performance at a scale that powers businesses and AI workloads globally. I've led efforts that delivered measurable revenue impact and drastically reduced inference latency for enterprise AI customers.

Before Microsoft, I spent four years at Morgan Stanley building real-time risk systems for prime brokerage, and interned at AWS building new Git merge APIs for CodeCommit.

I'm equally at home deep in C++ kernel paths as I am designing microservice architectures or shipping AI-assisted tools.

$1B+
Azure Files revenue contribution
35–40%
AI inference latency reduction
8x
Calc engine speedup (eTrade)
8+
Years Python · C++ · Java
Career

Work Experience

Microsoft
Software Engineer 2
Azure Files Storage · AI Infrastructure · Jersey City, NJ
Oct 2022 – Present
  • Optimizing Azure File storage at global scale. Built and deployed features providing latency, throughput and IOPS improvements for SMB, NFS, and REST protocols. Improved SMB write performance by 30% and REST write by 15% — contributing to $1B+ in Azure Files revenue.
  • Led revamp of Azure Files AI infrastructure to support OpenAI and enterprise AI workloads. Delivered 35–40% reduction in server latency for all AI inference workloads globally.
  • Partnered with engineering teams globally to identify flow bottlenecks and enhance developer productivity across distributed storage systems.
  • Deployed first-party application pipelines globally enabling MSGraph for Azure Files. Part of the team that brought Entra ID authentication for Azure Files.
🏦 Morgan Stanley
Software Engineer
Prime Brokerage & Wealth Management Risk Platforms
Aug 2018 – Oct 2022
  • Maintained and improved real-time and batch risk calculation platforms and margin engines for prime brokerage and wealth management divisions.
  • Built and operated Jenkins CI/CD pipelines for microservices handling global exchange data across NY, London, and Hong Kong.
  • Enhanced the NetClient C++ diagnostics library, building Python wrappers for network-level monitoring and observability.
📦 Amazon Web Services
SDE Intern — AWS CodeCommit
Seattle, WA
May 2017 – Aug 2017
  • AWS CodeCommit Team. Developed three new Git merge APIs: Squash merge, Two-branch merge, and Rebase merge — shipped to production.
  • Worked across the stack with Coral, Brazil (internal build system), AWS S3, and AWS DynamoDB.
Work

Work Projects

🤖
XFiles AI Assistant
Built an AI assistant leveraging 2 years of incident data, with an automated vector store built from Azure Files TSGs, internal docs, and public documentation. Led to rapid resolution of customer incidents at Microsoft scale.
AI / RAG Vector Store Azure Python
⚡
Etrade Calc Engine
Lead engineer on the scalability and optimization squad for integrating eTrade accounts post-merger at Morgan Stanley. Achieved an 8x improvement in account calculation time through algorithmic and architectural overhaul.
Performance Distributed Systems Java
🧪
Regression Framework
Built from scratch as a standalone microservice at Morgan Stanley — triggered via API or scheduled batch 3x/day for NY, London, and Hong Kong exchanges. Provided automated regression testing across risk calculation pipelines.
Microservices CI/CD Python Jenkins
🖼️
GraffitiMatch
Neural network to classify San Francisco graffiti images from a 30k image dataset. Winner at TechCrunch Disrupt Hackathon.
🏆 TC Disrupt Winner Neural Networks Python
📊
Business Recommendation System
Built on the Yelp dataset. Research published: "Predicting business success using Predictive data analytics" — applied ML to predict business outcomes from review and behavioral data.
📄 Published Paper Machine Learning Data Analytics
Personal

🚀 Hobby Projects

✨ built for fun
View all projects with screenshots →
☁️
VM Manager
Azure spot VM dashboard for hunting and securing spot instances across 35 regions. Saves up to 90% on cloud compute costs — built because cloud bills are too high.
Multi-region spot instance allocation
Real-time cost tracking dashboard
Offline snapshot mode
Guided setup checklist
Node.js Azure CLI 35 Regions Up to 90% cost savings
🎬
AI Video Analyzer
Video analysis tool with session management and intelligent chunking for long-form content. Built for processing hours of video with AI — because normal tools give up at 10 minutes.
Session persistence across runs
Smart video chunking for large files
Multi-format support
AI-powered content analysis
Python AI / LLM Video Processing
🚨
AI-Powered Livesite Triage
Built an AI system that automatically triages and repairs livesite incidents at Microsoft scale. Instead of engineers spending hours reading alerts and digging through logs, the system ingests incident signals, cross-references historical patterns, and surfaces a ranked action plan — cutting mean-time-to-mitigate significantly.
Automated root cause analysis from logs + telemetry
Historical incident pattern matching
Actionable repair recommendations
Reduced on-call toil for engineering teams
Python LLM + RAG Azure Monitor Vector Search
⚡
AI-Driven Engineering Workflows
Embedded AI across the full engineering lifecycle — from automated A/B test analysis and performance regression detection to AI-assisted code review and feature scoping. Not just using AI tools, but building pipelines that make the whole team move faster and ship with more confidence.
A/B test analysis: automated significance + recommendation
Performance regression detection with AI-generated diff summaries
Feature ideation pipelines grounded in customer signals
AI-assisted code review and documentation generation
Python GPT-4 Data Analysis CI/CD
Tech Stack

Skills & Tools

Languages
Python 8+ yrs C++ 8+ yrs C Java 8+ yrs Ruby
Frameworks & Libraries
Angular Flask Node.js
Cloud & Infrastructure
Azure AWS Azure Files Azure CLI AWS S3 AWS DynamoDB GitHub Actions CI/CD Jenkins Microservices
Systems & Protocols
SMB NFS REST Distributed Systems Storage Systems
AI & Data
LLM Orchestration RAG / Vector Stores Multi-Agent Systems Machine Learning Neural Networks Predictive Analytics
Academic

Education

New York University
Master of Science, Computer Science
May 2018 · New York, NY
PICT Pune
Bachelor of Engineering, Computer Science
May 2016 · Pune, India
Get in Touch

Let's Connect

Whether you want to talk storage systems, AI infrastructure, hobby projects, or just grab a virtual coffee — my inbox is open.

karan.chaudhari@nyu.edu github.com/krnch LinkedIn 🌐 krnch.me Download Resume