I'm a Software Development Engineer II at Amazon Web Services in New York, currently leading core product infrastructure for Kiro Autonomous Agent - AWS's autonomous AI development agent. I enjoy building scalable systems that bridge the gap between cutting-edge AI and practical developer tools.
My expertise spans full-stack development with TypeScript, Python, and Java, along with deep experience in AWS cloud infrastructure including CDK, Lambda, DynamoDB, and distributed systems. I've architected solutions that improved performance by 40% and scaled platforms to support 20,000+ users at launch.
Beyond my day job, I'm passionate about Machine Learning, NLP, and building side projects that solve real problems. I hold a Master's in Computer Science from NC State University (4.0 GPA) and love mentoring engineers and driving operational excellence initiatives.
CGPA: 4.0/4.0
Relevant Courses: Software Engineering, Artificial Intelligence, Database Management System, Computer Networks
CGPA: 8.57/10.0
Relevant Courses: Linear Algebra, Data Structures, Theory of Computation, Design and Analysis of Algorithms, Machine Learning, Information Retrieval Systems, Deep Learning
The paper was presented in the 25th International Symposium on Frontiers of Research in Speech and Music (FRSM 2020), jointly organized by National Institute of Technology, Silchar, India, during 8–9 October 2020.
Built a multi-market portfolio tracker (NSE, NASDAQ, NYSE) with Next.js 14, Supabase, and TradingView charts, supporting real-time P&L tracking, cash allocation, and transaction management — deployed on Vercel.
The repository contains notebooks of various MachineHack Hackathons covering multiple domains of Machine Learning and Deep Learning Worlds:
The project involves developing an android app that displays all the songs stored in the local storage that can be played using a minimalistic user interface. The interface allows users to like the songs they hear and those songs are then saved onto an online database. These liked songs were then compared with other users who liked similar songs to provide new recommendations using Machine Learning algorithm.
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The project was a research-oriented work that involved extracting cepstral features from audio obtained from the ASVSpoof 2017 benchmark dataset, which were then analyzed to find out which features better affect the decision of whether the spoken speech is genuine or spoof.
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The project involves developing a N-gram probabilistic model that predicts the next possible words based on the entered word or a sentence by the user. The prediction made by a pre-trained model trained on the text of multiple storybooks.
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The project utilizes a combination of python and natural language processing to create a custom model that helps machine classify text based on person, location, money, time, date and much more. We show the use of Bidirectional LSTM and BERT Models to overcome the problem. The project can be launched as demo version using Gradio.
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