
Researching and building applications that solve real-world problems.
Actively looking for Summer 2026 internships
The world of Machine Learning and AI has always inspired me to explore how far we can push its boundaries. I love to research new ideas and continually challenge myself to learn and apply state-of-the-art techniques.
I hold a Bachelor's in Computer Engineering from Pune University and am currently pursuing an M.S. in Computer Science at San José State University.
My hands-on experience spans Machine Learning and Deep Learning, with applications in Computer Vision, Natural Language Processing, and multimodal AI. Beyond AI, I'm proficient in full-stack development (MERN), cloud platforms (AWS), and orchestration tools (Docker, Kubernetes).
As a software engineer, I'm dedicated to building impactful applications that solve real-world problems. My focus is on advancing machine learning and multimodal AI, integrating these technologies to drive innovation and deliver value.

Led a team of 6 in developing a remote generator monitoring system with anomaly detection, remaining useful life prediction, and predictive maintenance models, achieving 93% accuracy on 500+ hours of operational data
Built an ANPR application using YOLOv11 and PaddleOCR with preprocessing pipelines, improving plate detection and text extraction to 97% accuracy
Eliminated 100% of manual intervention in LPG cylinder inspection by designing a ROS-based pre-filling pipeline with NVIDIA Triton Server, YOLO for defect detection, and EasyOCR for text extraction, producing 93% model
Automated an anomaly detection pipeline for copper coils by leveraging PatchCore and YOLO for defect identification and object tracking, with image compression to optimize performance
Engineered a real-time video streaming pipeline (Flask, React, Node.js, Express) with inference latency to 50ms, enabling smooth end-to-end deployment
Researched and evaluated pathfinding and collision avoidance algorithms for an autonomous boat navigation system
Built scalable computer vision systems including face recognition attendance (95% accuracy) and freezer space estimation (90% + accuracy, +30% efficiency), integrating with real-time pipelines
Engineered OCR and text-processing workflows for resume parsing, improving extraction accuracy by 85% and automating 70% of manual data entry
Developed and deployed YOLO models for industrial automation, achieving 92%+ detection accuracy and streamlining inventory tracking through end-to-end ML pipelines
Regression, Classification, Support Vector Machines, Decision Trees, Hidden Markov Models, Random Forest, Ensemble Learning, Dimensionality Reduction, Unsupervised Learning, Deep Learning, Computer Vision, Transformers, Auto-encoders & GANs, Natural Language Processing, RL Algorithms, Time series data analysis
TensorFlow, Keras, PyTorch, ROS, Scikit-Learn, NumPy, Pandas, Matplotlib, React, Express, Node.js, Next.js, Flask, spaCy, NLTK, LangChain, Streamlit
MySQL, SQL Alchemy, MongoDB, Mongoose, PostgreSQL, Prisma, Redis, AWS DynamoDB
AWS (EC2, RDS, Amplify, API Gateway, Cognito), Cloudflare, Git, Docker, Nginx, Kubernetes
Python, C++, JavaScript, TypeScript, Go, HTML/CSS
Recognized for both academic and extra-curricular performance throughtout the 4 years of undergraduation
Led cross-functional team of 6 engineers in developing industrial ML solutions, managing project timelines and deliverables
Led diverse technical and social programming to give students broad exposure to technology, leadership, and community engagement.