Jan 2024-Present

Velmeni Inc. | Remote

Software Engineer

1. Spearheaded the integration of PACS servers with the company's AI-driven report generation module using Django REST Framework and Python, delivering the company’s first live project and generating $80,000 in recurring annual revenue by streamlining medical data workflows, handling 500+ report generations per month from a single client.
2. Engineered a high-performance syncing service that integrates with dental imaging software using Python, Django, Celery, and AWS (S3, Lambda, SQS), ensuring 99.9% reliability in real-time file synchronization, with the ability to handle 50+ scans per minute, ensuring robust system reliability and scalability.
3. Achieved $100,000 in annual cost savings by developing the Velmeni Bridge in-house, eliminating third-party dependencies. Deployed the service as a Windows Service integrated with PowerShell scripts, ensuring continuous availability and operational efficiency.
4. Contributed to the development of Velmeni SD-API V2, focusing on building robust ModelViewSets, Django signals, and optimizing SQL queries. Utilized Django’s MVC architecture to handle patients and scan data effectively.
5. Implemented a robust CI/CD pipeline for Velmeni Bridge 2.0 using GitHub Actions, automating testing and deployments. Worked on Docker configurations to streamline the CI/CD process for SD-API V2, improving deployment efficiency.
6. Enhanced system fault tolerance by implementing exponential backoff retry mechanisms, task scheduling, and detailed error logging, reducing downtime resolution by 92% and significantly improving reliability in healthcare data processing.

May 2022 - Aug 2022

IBM | Almaden,San Jose

Graduate Extern (Internship)

1. Explored Federated Unlearning with IBM.
2. Used PyTorch and Matplotlib for model customization and visualization.
3. Implemented Grid Search for hyperparameter tuning.
4. Built a Python-based evaluation method for Federated Unlearning.

Cloud resume project

1. Engineered a dynamic, cloud-hosted resume leveraging AWS S3 and CloudFront.
2. Automated serverless infrastructure deployment using AWS SAM.
3. Streamlined a CI/CD pipeline for swift and reliable updates.
4. Configured AWS Route 53 for optimal global domain management.
5. Demonstrated advanced serverless architecture and DevOps best practices.

Implementing k-medoids clustering in parallel

1. Wrote parallel implementations of k-medoid clustering components using CUDA and MPI.
2. Wrote a parallel implementation of silhouette coefficient using MPI.
3. Used rice gene expression dataset to evaluate the implementation. Achieved highest silhouette score of 0.65.
4. Achieved a significant speedup of 7x for clustering and 20x for evaluation compared to serial implementation.