// based in Bengaluru, KA
Java Backend Developer & AI Integration Engineer
Engineering production-grade microservices with Spring Boot & Spring Cloud — and integrating LLMs, RAG pipelines, and Spring AI into scalable, cloud-native backend systems.
I'm Mohammad Anzar, a Java backend developer based in Bengaluru, Karnataka, pursuing a B.Tech in Information Science at RNS Institute of Technology (CGPA: 9.3/10). I specialise in building production-grade microservices with Spring Boot, Spring Cloud, and event-driven architectures using Kafka and Redis.
Beyond backend systems, I integrate Large Language Models and AI capabilities into enterprise applications using Spring AI, RAG pipelines, and LLM orchestration — bringing intelligent, context-aware features into scalable backend services.
I also have hands-on experience with machine learning, including federated learning and PyTorch-based pipelines. Outside of work, I compete in DSA competitions, win project hackathons, and bring both backend depth and a product-thinking mindset to every project.
A distributed food delivery platform built with Spring Boot microservices — individual services for users, restaurants, orders, payments, and delivery partners, wired together via API Gateway and service discovery. Features real-time order tracking over WebSockets, JWT-based authentication, and Docker containerisation for scalable fault-tolerant deployment across all services.
A multi-tenant distributed job scheduling platform using Java (Spring Boot), enabling automated execution, retry handling, and real-time monitoring of cron-based tasks across tenants. Implements a scalable, event-driven architecture leveraging Apache Kafka, Quartz Scheduler, and Redis for asynchronous job execution, fault tolerance, rate limiting, and high system reliability. Full-stack cloud-native solution with React, Docker, Kubernetes, and CI/CD pipelines (GitHub Actions), incorporating real-time WebSocket updates and observability using Prometheus and Grafana.
A federated learning pipeline using PyTorch and EfficientNet that trains models on distributed healthcare IoT data without centralising sensitive information. Implements Differential Privacy and Secure Aggregation for data confidentiality — achieving stable convergence on non-IID datasets across multiple clients.
A MERN stack AI web application that teaches financial literacy through interactive lessons, quizzes, and gamification. Users earn points and badges for completing modules on budgeting, investing, and credit management. Built with React frontend, Node.js/Express backend, MongoDB database, and integrated with OpenAI's API for personalized learning paths.
A MERN stack web application that provides real-time safety information for travelers. Users can view crime rates, health advisories, and emergency contacts for their destination. The app integrates OpenAI's API to offer personalized safety tips based on user profiles and travel plans.
A full-stack MERN hospital management system handling patient records, appointment scheduling, doctor availability, and billing. RESTful APIs with Node.js/Express, MongoDB for data persistence, role-based authentication, real-time updates, and analytics dashboards for data-driven administration.
Open to internships, collaborations, and full-time roles. If you have an interesting problem to solve, I want to hear about it.