Architected and deployed a full-stack batch processing system utilizing NextJS for the front end, Go for the back end, MongoDB for data storage, and Kafka for real-time event streaming. The system optimized bulk file uploads by leveraging streaming and buffering techniques, resulting in high-throughput processing.Refactored a monolithic application into a microservices architecture, employing Docker for containerization and Kubernetes for orchestration. This transformation led to a 40% increase in system performance by enhancing scalability and fault isolation.Developed a custom backend framework using Go and gRPC, which streamlined communication between services and reduced latency by 20% through optimized RPC handling.Enhanced API documentation and testing workflows by integrating SwaggerUI with gRPC services, leading to a 15% boost in development productivity through improved API clarity and testing efficiency.Engineered a backend module for loan management operations using Go and gRPC, achieving a 30% increase in processing speed and improving transaction accuracy to 90% by optimizing data validation and RPC communication.Implemented Kafka log monitoring with Grafana Dashboard, containerized with Docker, to provide real-time insights into streaming data and reduce troubleshooting time by 25% through enhanced visibility and alerting mechanisms.

Developed a React-based user interface to streamline doctor-patient interactions, optimizing the appointment scheduling process and achieving a 30% reduction in booking time through improved UX design and efficient state management.Integrated Razorpay into the system for seamless transaction processing, resulting in a 20% increase in payment success rates by leveraging optimized payment gateways and error-handling mechanisms.Architected Progressive Web Applications (PWAs) for bulk patient feedback collection, enhancing response rates by 25% through improved accessibility and offline capabilities.Implemented an AWS Lambda function for automated processing of feedback files, improving data accuracy and reducing processing time by 40% through parallel execution and optimized data handling techniques.Engineered a WhatsApp chatbot for appointment scheduling, achieving a 90% user satisfaction rate by utilizing natural language processing and efficient backend integration for real-time booking.Built a React-based receptionist portal, reducing the booking process time by 15 minutes through streamlined user workflows and optimized form handling.Enhanced payment processing within the receptionist portal by integrating Razorpay, increasing transaction volumes by 15% through improved API integration and real-time payment validation.

Designed and developed a Python-based API leveraging Natural Language Processing (NLP) techniques for text analysis, which reduced errors in GST filing by 90% through advanced data parsing and semantic analysis.Engineered an automated AWS Lambda function for bi-daily S3 bucket data migration, incorporating data integrity checks and ensuring high availability across cloud storage layers.Boosted user engagement by 30% through the development of a dynamic dashboard built with React and D3.js, facilitating real-time tracking and visualization of historical GST filing statuses with interactive data representation.

Orchestrated data acquisition processes from both primary and secondary sources, ensuring the seamless integration and maintenance of databases and data systems using SQL, NoSQL, and RESTful APIs.Analyzed complex datasets and transformed them into actionable insights by applying statistical modeling techniques using Python, R, and advanced data visualization tools.Optimized data pipelines by performing data cleaning, preprocessing, and issue resolution utilizing Python's pandas library and SQL queries, ensuring data accuracy and consistency across the system.

Engineered convolutional neural networks (CNNs) with TensorFlow for a location prediction project, leveraging image data provided by users to generate accurate location forecasts.Optimized image recognition and classification models using Keras and OpenCV, achieving 80-85% accuracy on labeled test datasets featuring urban and rural scenes through fine-tuning model architectures and enhancing preprocessing techniques.