About Me
I am a Software Engineer and AI practitioner with over a decade of experience building resilient, data-intensive systems. Currently, I work at Meta, architecting AI-native infrastructure with a focus on privacy stacks and addressing hallucinations in Large Language Models. Beyond the enterprise scale, I am obsessed with the democratization of AI. I spend my time exploring Multi-Agent Systems and local LLM optimizations, building practical tools that cut through complexity and streamline day-to-day toil.
Technical Arsenal
Work Experience
Meta Platforms
Jan 2022 - PresentBuilding AI native infrastructure to streamline data flow in Meta’s ads stack. Addressing hallucinations for MetaAI using LLM Judges for benchmarking. Built ML Feature Privacy Stack for offline ads data privacy.
Yelp Inc
March 2019 - Jan 2022Backend development of Ads platform. Built ad attribution infrastructure for external retargeting ads and worked on third-party cookie replacement solutions.
Casenet LLC
Jan 2018 - March 2019Internal web applications on MongoDB, Go, and Angular 6. Backend APIs for Health Management Application Trucare using Java 8.
Sabre Inc
May 2015 - Jan 2018Full stack development in React and .NET for internal hospitality solutions. Developed internal windows applications in C# and Ruby.
IBM India Software Labs
July 2012 - March 2014Enhanced and fixed AIX commands, libraries, and shell related issues using C.
Featured Projects
Multi-step conversational interview prep app using LLM and Gemini API.
Claude Desktop connectors implementation including Filesystem and other tools.
Hierarchical Concurrent Software Engineering Team using CrewAI.
A web app for stock suggestions using Reddit data analysis.
Multi-agent research assistant system utilizing LLMs.
Collection of deep learning experiments and implementations.
MCP server implementation for file system interaction.
Writing & Research
Recent Articles
Case Study: Building a Multi-Agent Research Assistant with A2A Protocol
Exploring the implementation of multi-agent systems using the A2A protocol.
A Look at Google’s Agent-to-Agent (A2A) Protocol for Multi-Agent AI Systems
Deep dive into Google's A2A protocol specifications and potential.
Model Context Protocol (MCP) in Practice
A hands-on tutorial building a MacOS file assistant with MCP.
Fine-Tuning Mistral 7B on Apple Silicon
Guide to fine-tuning large language models on local Mac hardware.
Building and Running CodeLlama Locally on Your Mac
Step-by-step guide to running CodeLlama locally.
Publications & Reviews
Proposed Challenges And Areas of Concern in Operating System Research and Development
Conference Reviews
- ACM SIGCSE TS 202610 paper reviews
- IEEE SysCon 20262 paper reviews