Computer Science student specializing in Cloud Computing and Automation with research experience in cloud anomaly detection and MLOps. Proven track record in developing scalable cloud-native applications and implementing automated infrastructure using Terraform and AWS.
Indian Institute of Technology (IIT) Roorkee
Roorkee, India
Contributing to the design and development of ADAPT-Cloud, a hierarchical framework for real-time cloud anomaly detection using dual-attention neural networks and PPO. Designing a three-stage anomaly detection pipeline integrating statistical filtering and reinforcement learning. Implementing scalable experimentation using PyTorch, Docker, Kubernetes, AWS, and GCP.
Allegheny College
Remote
Completed research training in Responsible Conduct of Research (RCR), scientific ethics, literature review, and research methodologies through an international summer research program.
Bachelor of Technology
Specialization: Cloud Computing & Automation. CGPA: 9.02/10. Relevant Coursework: Operating Systems, Computer Networks, Distributed Systems, Cloud Computing, DBMS, Machine Learning.
ISC (Class XII)
Percentage: 92.75%
ICSE (Class X)
Percentage: 96.66%
Python, TensorFlow, UNet++, DVC, DagsHub, Terraform, GitHub Actions
Designed and deployed an end-to-end MLOps pipeline for automatic brain tumor segmentation. Implemented dataset versioning and provisioned cloud infrastructure using Terraform.
Python, AWS, Azure, GCP, Terraform, Streamlit
Designed a multi-cloud benchmarking framework to evaluate compute, storage, and networking performance. Automated resource provisioning using provider-agnostic Terraform modules.
React, Node.js, AWS, Docker, Terraform, PostgreSQL
Built a secure cloud-native file sharing platform supporting encrypted uploads and automatic file expiration. Integrated AWS S3, Lambda, and App Runner.
Flask, AWS, Docker, Terraform, PostgreSQL
Developed a production-grade cloud-native URL shortening service using a scalable three-tier AWS architecture with asynchronous click analytics.