I build backend systems that stay correct when everything happens at once.
Backend engineer focused on Java and Spring Boot — event-driven microservices, Kafka pipelines, and Redis-backed caching, built to hold up under concurrent load and to fail without losing data.
The short version
I'm a backend engineer and Computer Science graduate from VIT-AP, and most of what I build lives on the backend — the part of the system a user never sees, but always feels when it's slow or wrong.
I like the problems that show up once you have more than one service: two consumers racing for the same message, a payment failing halfway through an order, a cache going stale under load. My two main projects are built around solving exactly that — with Kafka for event delivery, Redis for locking and caching, and Spring Security for keeping the API honest.
I care about the numbers as much as the architecture diagram — load-tested, measured, and documented, not just "should work."
What I build with
Java · Spring Boot · Hibernate · MySQL · PostgreSQL · Redis · Apache Kafka · Docker · Git · GitHub Actions · JUnit · Swagger · JWT
+ also: Spring Security · Spring Data JPA · SQL · Docker Compose · Mockito · JMeter · REST APIs · Microservices · TDD · OOP · SOLID · System Design · DSA · Multithreading
$ ls ./projects
Event-Driven Order Processing System
Persisted locally. No outbound calls.
One event. The only contract between services.
Inventory + Payment consume in parallel. Zero lag.
Payment fails → automatic rollback. No orchestrator.
Distributed Job Scheduler
Client → Auth → Job API. Saved as SCHEDULED.
Quartz JDBC store, clustered mode.
Both instances fire. They race for the job.
Redis SETNX — only one instance wins.
Education & credentials
Covers index strategy, replication, sharding, and day-to-day cluster administration — validated through MongoDB University's proctored exam.
Core OCI concepts — compute, storage, networking, security, and pricing on Oracle's cloud platform.
Deploying and working with generative AI models and OCI's AI services in production.
Ongoing practice across arrays, trees, graphs, and dynamic programming — the same muscle that shows up in interview rounds and in tracing a bug through a live system.