👋 Welcome to my GitHub profile! I'm a passionate Software Developer with a keen interest in developing efficient and scalable systems and microservices.
- Building high-performance, scalable systems
- Exploring microservices architecture
- Delving into cloud computing and DevOps
- Implementing cutting-edge AI and machine learning models
- Email: bhagyatrivedi20273@gmail.com or trivedi.bh@northeastern.edu
- GitHub: Bhagyatrivedi27 or bhagyatrivedi
- LinkedIn: bhagyatrivedi27
- Portfolio: bhagyatrivedi.netlify.app
- Real-Time Chat Application: Developed a chat application serving over 100 users using Node.js, Express.js, and Socket.IO, reducing message delivery time by 50%. Integrated OpenAI's GPT-4 for real-time translation, enhancing communication efficiency by 40%.
- NLP-Driven Survey Platform: Engineered a survey platform using Python and Flask on AWS EC2 and AWS RDS, improving form accuracy by 40% through real-time analytics and machine learning model comparisons.
- Data Visualization: Visualized research and participant data using Plotly on 5+ LLM responses, enhancing readability by 30%.
- Data Loader Modernization: Modernized a data loader for 120+ business objects using Spring-Boot and Spring JDBC, reducing costs by 100% and doubling performance.
- Pandemic-EBT Citizen Portal: Led the development of a portal for 475,000+ users using Angular and Spring framework, optimizing the process with Jenkins to reduce deployment time by 92%.
- Technology Stack: Go, Postgres, AWS EC2, EBS, RDS, S3, ELB, SES, DNS Route53
- Description: Deployed a RESTful CRUD customer management web application on AWS, leveraging Go and Postgres. Engineered a Terraform template for various AWS services and implemented CI/CD pipeline using GitHub Actions for automated build, test, and deployment processes.
- Technology Stack: Node.js, React, Express, MongoDB, AWS S3
- Description: Developed an AI-powered movie ticketing app integrating AWS S3 for image management, increasing user engagement by 40% through personalized recommendations. Reduced image load times by 50% and increased ticket sales by 25% with efficient image handling.
- Technology Stack: Deep Learning, PyTorch, Neural Networks
- Description: Architected a real-time energy management algorithm using N-Beats architecture to enhance battery load forecasting for residential energy systems. Improved performance using Conformal Quantile Regression (CQR) model by 20% across all load types.