I’m Nikhil Sai Nambula, a Software Developer with 3 years of experience building reliable, scalable, and fault-tolerant backend systems. I specialize in Java, Spring Boot, microservices, distributed systems, and AWS, with hands-on experience developing high-scale transaction-processing services for financial platforms. I enjoy solving complex engineering challenges and designing systems that are resilient, performant, and maintainable at scale. I’ve solved 1000+ LeetCode problems, earned the AWS Certified Developer – Associate certification, and continuously build projects that help me explore new technologies, system design concepts, and architectural approaches.
Software Developer
TCS
- Developed and maintained scalable Java 17, Spring Boot, and Microservices applications for a financial transaction platform serving 2M+ users and 50K+ daily requests.
- Designed and implemented RESTful APIs with robust validation, global exception handling, API versioning, and backward-compatible contracts for reliable customer-facing workflows.
- Eliminated N+1 query issues by optimizing JPA/Hibernate fetch strategies, reducing database round-trips from ~40 to ~8 per request and improving API latency by 30%.
- Optimized SQL queries and database interactions to improve data retrieval efficiency and application responsiveness across transaction-processing workflows.
- Designed and implemented a Redis distributed caching layer with custom TTL and eviction strategies, reducing primary database load by 40% and improving P99 performance.
- Secured REST APIs using Spring Security, JWT, and RBAC, protecting sensitive financial data and enforcing role-based access across backend services.
ShopVerse – E-Commerce Platform
ShopVerse is a full-stack e-commerce platform, It uses Spring Boot and MongoDB for the backend and persistence layer, with a responsive React and Material UI frontend. The project implements JWT authentication with refresh-token rotation, RBAC, Redis session caching, Cloudinary CDN integration, and a secure order state machine, with automated CI/CD through GitHub Actions.
AI-Powered Recipe Generation from Food Images
AI-Powered Recipe Generation from Food Images is a computer vision and deep learning project that generates food ingredients from images using a CNN-based image feature extraction pipeline and an LSTM sequence decoder. The system combines InceptionV3 and Xception architectures for visual feature extraction with sequence modeling to generate ingredient predictions. The model achieved 87% top-5 classification accuracy on a custom-labeled subset of the Food-101 dataset.
ApplyPilot
ApplyPilot is an AI-powered Chrome extension for intelligently autofilling job applications. It uses semantic field matching, confidence-based autofill strategies, DOM observation, and field verification to accurately populate dynamic application forms while keeping the user in control. The project includes a React side panel, Fastify API, shared TypeScript packages, a synthetic ATS test environment, and automated Vitest/Playwright testing.
B Tech (CSE)
SRKR Engineering College
AWS
Mar 2026
Attendee · Oct 24, 2026