About
AI Engineer and Researcher – specialist in Python Development, Agentic AI and MLOps Cloud Engineering. 3 years of experience with Python, 3 years of experience with Machine Learning pipelines and PyTorch, 2 years of experience with RAG systems. Available for Full time roles starting May 2026.
Research
- SmolSolver - Mathematical Reasoning with SLM models [report] (Sep 2025 - Dec 2025): Created 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 [report] (Sep 2025 - Dec 2025): Benchmarked 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.
Professional Experience
Machine Learning Teaching Assistant | NYU Tandon School of Engineering
Sep 2025 - Present
- Helped students understand Machine Learning concepts (PyTorch, Pandas, Numpy, etc.)
- Held weekly office hours to clear students' doubts in niche ML topics, and issues with code for assignments and project
- Created Video lectures on common doubts in Machine Learning
Co-Founder | Ingelt Board
Dec 2022 - Jul 2024
- Architected and deployed secure workflows for Teachers, Students, and Management, featuring real-time capabilities: Group Chat and Live Streaming
- Designed a cloud-native architecture using AWS S3 for scalable content storage mapped via RDS, and ensuring high availability through region-specific EC2 instance management
- Implemented secure infrastructure with SSL/TLS roles and security groups, while integrating stripe payment gateway to handle secure transactions
Co-Founder | Macverin Technologies
Jul 2022 - Dec 2022
- Led technical strategy and system design, architecting scalable distributed solutions using versatile backend stack (Java Spring, Python Django) to streamline business workflows
- Engineered full-stack architectures using JavaScript ecosystem (TypeScript, React, Next.js, Express), creating modular, type-safe interfaces backed by robust APIs to handle data-intensive workloads
- Directed end-to-end product delivery and DevOps, managing complete SDLC from UI/UX design (Figma) to cloud deployment (AWS, Docker), and establishing automated CI/CD pipelines that ensured 99.9% system availability
Technical Skills
Programming Languages
Python, Java, C++, JavaScript, TypeScript
Applied AI and Research
PyTorch, TensorFlow, Fine-Tuning, RAG, Language Models, Vision Models, Langraph
Development & Operations
AWS, GCP, Prometheus, Grafana, Jenkins, Github Actions, Docker, Kubernetes, Ansible, Terraform
Frontend Frameworks
Next.js, Node.js, FastAPI, Django, Flask, SQL, NoSQL
Projects & Publications - Selected Work
2025 (In-Progress)
Your Personal Research Assistant. Why would you use it over any traditional Generative AI tools?
It is specifically tailored to minimize hallucinations and have a directed research.
Usecases:
Usecases:
- General Lifestyle
- Study
- Research
[ project page ]
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.
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 ]
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.
Teaching & Technical Leadership
Nov 2025 · Featured Technical Deep Dive
A 17-minute deep dive into the calculus and geometric intuition behind Ridge (L2) and Lasso (L1)
regularization.
I breakdown the gradient updates to explain why L1 leads to sparsity while L2 shrinks
weights, backed by
Python experiments on the Diabetes dataset.
[ download slides ]
[ watch on youtube ]
- Graduate Course Assistant (ML) @ NYU Tandon: Mentoring 50+ students. Key contribution: Created supplementary video lectures to simplify complex calculus concepts for the cohort.
- Web Development Mentor: Taught 120+ students, enabling deployment of 40+ web apps.
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.