About

I am a graduate student in Computer Engineering at the NYU Tandon School of Engineering, specializing in Applied AI systems, Deep Learning, and Natural Language Processing (NLP) / SLM / LLM Research.

My technical foundation was forged during my undergraduate studies, where I co-founded the startup InGelt. In that role, I applied hands-on expertise in Systems Design, Web Technologies, Cloud, and DevOps to build and scale a real-world product.

I'm driven by the challenge of making AI systems that actually work in the real world—not just in research papers. Whether it's helping small businesses adopt machine learning or building tools that turn weeks of research into hours, I want to bridge the gap between cutting-edge models and practical impact. My work comes from the belief that the best technology is the kind that solves real problems for real people, not just impressive benchmarks.

Currently seeking opportunities in Applied AI Research, Machine Learning Engineering, and AI Product Development for full-time roles starting 2026.

Current Research

  • SmolSolver - Mathematical Reasoning with SLM models [recent development]: Currently working to create a Generator + Verifier SLM Models finetuned on PRM800K dataset and GSM8K dataset for Mathematical Reasoning and Step by Step Evaluation.
  • Cross-Domain Vision - Image Reconstruction Benchmark [recent development]: Benchmarking SoTA models (CNNs, Transformers, Diffusion) on super-resolution, denoising, and inpainting tasks across diverse domains (natural scenes, text, astronomy, art) to analyze cross-domain robustness and failure patterns.
  • OS Security Profiler - Mitigation Impact Analysis [recent development]: Analyzing the performance overhead of transient execution attack mitigations by proposing an eBPF-based profiler to classify workloads by system call frequency, enabling informed security vs. performance trade-offs.

Technical Skills

Programming Languages

Python, Java, C++, JavaScript, TypeScript, MATLAB, Statistical Analysis

AI & Machine Learning

PyTorch, TensorFlow, LangGraph, Transformers, CNNs/RNNs, GPT, CLIP, YOLOv8, RAG Architectures, LoRA Fine-tuning

Development & Operations

React, Node.js, Django, Flask, FastAPI, SQL, Docker, Kubernetes, Jenkins, AWS (EC2, RDS, S3), CI/CD Pipelines

Research & Analytics

Jupyter, Git, LaTeX, Data Visualization, Experimental Design, Research Methodologies, MLflow, Prometheus, Grafana

Projects & Publications - Selected Work

Wort - Deep Research Assistant
2025 (In-Progress)
Building an AI research assistant that actually understands how researchers work. Instead of just searching, it uses Chain-of-Thought and Tree-of-Thought reasoning combined with GraphRAG to navigate sources, verify claims, and synthesize findings like a human would. Powered by Gemini-2.5-Flash and Gemini-2.5-Pro, it's turning what used to be weeks of literature review into hours of focused work.
[ project page ]
Finassistant - RAG-based Financial Agent
2025 (In-Progress)
Created a financial analysis tool that speaks your language—literally. It pulls real-time data from SEC Filings and macro APIs, searches through financial news, and answers questions about portfolios and risk in plain English. The conversational interface makes institutional-grade analysis accessible. Currently fine-tuning it on sentiment-labeled datasets so it can better read between the lines of earnings calls and market commentary.
[ live demo ] [ code ]
Snap2Caption - ML Systems for Caption Generation
2025
Built a complete ML pipeline that turns photos into Instagram-ready captions and hashtags in under 2 seconds. Fine-tuned LLaVA-1.5/1.6 (7B parameter) vision models using LoRA—a technique that makes training efficient without losing quality—on 100k urban images. The production setup handles 300+ requests per hour and includes the full monitoring stack (MLflow, Prometheus, Grafana) so we know exactly how it's performing. Infrastructure is fully automated with Terraform on GPU clusters.
[ code ]
Study on Long-Tail Problem of AI for Small-Scale E-commerce
Published Research (2024)
Explored why AI models struggle when small businesses try to use them. The problem? These models are trained on massive datasets, but small e-commerce stores have limited data and long-tail product distributions—lots of niche items with few examples. Tested classification models, recommendation engines, and LLM-powered support systems to understand where they break down. The published research gives practical guidance for small merchants trying to adopt AI without enterprise-level resources.
[ research paper ] [ review paper ] [ code ]

Professional Experience

Co-Founder and Lead Engineer
Ingelt Board | Delhi, India
Dec 2022 - Jul 2024
Co-founded and scaled an Ed-Tech SaaS platform from zero to 200+ enterprise clients and 5,000+ active students. Led a team of 6 engineers through the full product lifecycle—from architecture to deployment. Built three distinct user portals (Student, Teacher, Admin) using MERN stack and Django, orchestrated with Docker and Kubernetes for seamless scaling. Deployed production infrastructure on AWS (EC2, RDS, S3) with automated CI/CD pipelines via Jenkins and GitHub Actions, maintaining 99.9% uptime while accelerating deployment cycles.
Software Engineering Intern
Macverin Technologies | Hybrid (UP, India)
Jul 2022 - Dec 2022
Delivered Dockerized CMS and CRM platforms to 8 clients, improving their content management efficiency by 40%. Built client-facing analytics dashboards using Python and JavaScript (Chart.js) to visualize user behavior and sales metrics, enabling data-driven decision making for stakeholders.

Teaching & Mentorship

  • Graduate Course Assistant (Fall 2025 - Present): CS-GY 6923 Graduate Machine Learning, NYU Tandon.
  • Web Development Mentor (Oct 2022 - May 2023): Taught 120+ students, enabling deployment of 40+ web apps.
  • Python Programming Mentor (Aug 2021 - Oct 2022): Led hands-on workshops, hackathons, and real-world problem-solving.

Leadership & Service

Leadership

  • Technical Head (Jun 2023 - Jun 2024): Computer Society of India (CSI) Student Chapter. Led a 15-member team and organized 12+ technical events.
  • Technical Workforce Lead (Dec 2022 - Jul 2024): Ingelt Board. Led a technical workforce of 6 engineers.

Extracurriculars

  • Subject Matter Expert (Sep 2022 - Jul 2023): Chegg India. Delivered 1,000+ academic solutions in Computer Science and Mathematics.
  • Tech Blogger: Author articles on Startups, AI and Software Development at Medium.
  • LeetCode Contributor: Solved 200+ problems, with 52 solutions posted and 4.6K+ community views.