Building the future of Artificial General Intelligence at Amazon AGI, where I architect and scale core AI infrastructure powering next-generation AI systems that process and learn at unprecedented Amazon scale.
With over 5+ years of specialized experience in AI/ML engineering across Amazon, Nvidia, UBS, BNY Mellon, and innovative AI startups, I combine deep technical expertise in distributed AI systems, LLM infrastructure, multi-agent systems, and production ML systems with a track record of delivering high-impact solutions at scale.
At Amazon's AGI division, I engineer core infrastructure that powers next-generation AI systems at scale. My work focuses on building distributed systems that enable AI to learn, reason, and operate across diverse domains—pushing the boundaries of what's possible in artificial intelligence.
Interested in AGI, distributed AI systems, or building ML at scale? Let's connect.
Education
- MS in Artificial Intelligence | Khoury College of Computer Sciences, Northeastern University | GPA: 4.0/4.0
- B.E. (Hons) in Electrical & Electronics Engineering | BITS-Pilani | India's #1 Private University
My interdisciplinary background—from hardware architecture and electrical engineering to advanced AI—enables me to design optimized ML infrastructure and multi-agent systems that consider the full stack, from distributed system design and algorithm optimization to hardware acceleration on GPUs, TPUs, and custom AI accelerators.
Research
- Published IEEE research paper in communications engineering - demonstrating strong research foundations in mathematical modeling and signal processing that translate to AI systems design
Competition Wins
- 🥇 1st Place - UBS Superstars Hackathon | Real-time Sign Language Translation
- 🏆 Top 5 - Kaggle Image Popularity | Solution
- 🏆 Top 14/6,828 - Sales Forecasting Challenge | Code
Former President | Google Developer Student Club (GDSC), Northeastern University Led technical workshops, hackathons, and community building initiatives connecting 500+ students with cutting-edge technologies and industry best practices.
Graduate Teaching Assistant | Machine Learning & Data Mining (DS 4420) Mentored graduate students in advanced ML concepts, reinforcing best practices in model development, evaluation, and deployment.
Multi-Modal AI & Large Language Models
- Visual Question Answering (VQA) - Multi-modal Large Language Models for reasoning over visual and textual information
- LLM-Based Text Summarization & Translation - Built BART models and Transformers from scratch for advanced NLP tasks
- Natural Language Inference (NLI) - Multi-domain models for textual entailment (mNLI, sNLI)
Robotics & Computer Vision
- Instruction-Following Robot - Programmed Locobot WX250 for natural language command execution
- Real-Time Interview Emotion Detection - Live emotion recognition system for interview analytics
- Table Tennis Ball Tracking - High-speed object tracking for game analytics
- Classical Computer Vision Projects - AR Furniture placement, live cartoon filters
Applied ML & Data Science
- Medical MNIST Classification - Healthcare AI for medical imaging
- Open Document Sensitivity Classifier - Enterprise security ML system (UBS - Confidential)
- Microbe Analytics Data Engineering - Research-grade data pipelines for computational biology
- Deep Learning Specialization (Coursera)
- Azure AI Fundamentals
- Azure Data Fundamentals
- Google Cloud AI/ML
LinkedIn View Stats
Building AI systems that matter. Open to collaborations on challenging problems in AGI, multi-modal learning, and production ML systems.