Casey – React, Node.js, Typescript

This long-term project involved building a cloud-based microservices architecture with scalable ExpressJS APIs deployed on Microsoft Azure to optimize cost and performance. The Case Study tool, developed for a healthcare organization, enables users to generate case studies twice as fast using an AI-powered editor. The case study listing is a key feature, allowing users to efficiently search and retrieve relevant information. Additionally, the platform includes an AI-based search engine connected to 12 different datasets, enhancing data accessibility and insights.

• Developed scalable ExpressJS APIs within a microservices architecture, enabling efficient case study generation and management for a healthcare organization;
• Deployed infrastructure across Azure, reducing operational costs by 30% while ensuring high availability;
• Integrated an AI-based editor, accelerating case study creation by 2x and improving workflow efficiency for healthcare professionals;
• Implemented listing and search functionality, allowing users to query case studies with an AI-based search engine connected to 12 datasets, improving data retrieval speed by 40%;
• Containerzed backend application and configured for different environments using Docker, Kubernetes, and Terraform;
• Automated CI/CD pipelines using Azure pipelines, cutting deployment time by 35% and ensuring fault tolerance.

A task management application enabling real-time collaboration, task prioritization, and deadline tracking for teams, focusing on user-friendly design.

• Developed the front end with React and TypeScript, ensuring a responsive and intuitive UI;
• Built RESTful to handle task creation and updation using AWS Lambda and API Gateway;
• Used Node.js as a runtime environment on top of AWS Lambda;
• Integrated WebSocket for real-time updates, improving user collaboration efficiency.

A web application integrating Next.js with GraphQL and REST APIs is designed to deliver a high-performance user experience through optimized data fetching and rendering strategies. It also includes the feature to create dynamic forms, which was creating another form to store the configurations.

• Integrated Next.js with GraphQL and REST APIs, ensuring seamless data fetching and rendering;
• Implemented dynamic forms functionality using react-JSON schema-form, allowing users to create and manage configuration forms with a 30% reduction in setup time;
• Optimized performance using Next.js features like Static Site Generation (SSG) and Incremental Static Regeneration (ISR), improving page load speed by 25%;
• Enhanced user experience by implementing efficient data rendering strategies, reducing client-side processing time by 15%.

This project involved building a cloud-native asynchronous job processing system on Google Cloud Platform using Node.js microservices. The system provides an internal API for queuing and managing background tasks such as sending notifications, processing webhooks, and syncing data between services. Redis to manage job queues, retries, prioritization, and scheduled tasks. The services were deployed on Cloud Run for scalability and cost-efficiency, with infrastructure automated using Terraform.

• Developed and deployed microservices using Node.js and Express.js on GCP Cloud Run, enabling scalable async job handling;
• Integrated BullMQ with Redis to manage background jobs, implementing retry logic, job prioritization, and concurrency control;
• Designed REST APIs to queue tasks from other services, with support for different job types and scheduled processing;
• Built a separate worker service to process queued jobs and log execution results, improving task traceability;
• Containerized applications using Docker and managed infrastructure with Terraform for repeatable, consistent deployments;
• Improved background task processing speed by 40% and reduced system load by offloading non-critical tasks;
• Monitored job health and failures using GCP’s built-in logging and alerting tools, ensuring reliability and observability.

The project was centered around the development of a dynamic video editor application from scratch, enabling users to generate bulk videos automatically by creating video templates with variable elements. The application was built using React.js, leveraging FabricJS and the HTML5 Canvas element for advanced graphical editing. This project emphasized performance optimization, seamless user experience, and scalable bulk video generation, making it a powerful tool for automated content creation.

• Developed a feature-rich, template-based video editor using React.js and FabricJS for object manipulation;
• Built a custom video rendering pipeline using FFmpeg, converting FabricJS objects into 30 FPS video frames;
• Designed and implemented backend services in Node.js for scalable video processing and bulk automation;
• Created custom React hooks to optimize state management and improve performance;
• Integrated HTML5 Canvas for efficient rendering and smooth user interactions;
• Debugged and resolved performance bottlenecks to enhance overall system efficiency.