Backend Engineer specializing in distributed systems, microservices architecture, and event-driven design.
I build production-grade systems that scale — from order matching engines to distributed rate limiters.
name: Mutyalapati Akhil
role: Backend Engineer
university: Vellore Institute of Technology, Amaravati
degree: B.Tech Computer Science and Engineering
cgpa: 8.21 / 10.0
batch: 2023 - 2027
location: Amaravati, Andhra Pradesh, India
specialization:
- Java Spring Boot Microservices
- Distributed Systems Architecture
- Event-Driven Systems with Apache Kafka
- Real-Time Systems with WebSocket
- API Design and Rate Limiting
open_to:
- Backend Engineering Internships
- Software Engineering Roles
- Distributed Systems Projects
- Open Source CollaborationLanguages
Backend & Frameworks
Databases & Caching
Messaging & DevOps & Tooling
| Domain | Proficiency | Details |
|---|---|---|
| Microservices Architecture | ⭐⭐⭐⭐⭐ | Spring Boot, Eureka, OpenFeign, API Gateway |
| Event-Driven Systems | ⭐⭐⭐⭐⭐ | Apache Kafka, producers, consumers, topics |
| Distributed Caching | ⭐⭐⭐⭐⭐ | Redis, pub/sub, TTL, distributed counters |
| API Design | ⭐⭐⭐⭐⭐ | REST APIs, JWT, rate limiting, versioning |
| Real-Time Systems | ⭐⭐⭐⭐ | WebSocket, STOMP protocol, live data feeds |
| Database Design | ⭐⭐⭐⭐ | PostgreSQL, JPA, schema design, migrations |
| Containerization | ⭐⭐⭐⭐ | Docker, Docker Compose, multi-service setups |
| Security | ⭐⭐⭐⭐ | JWT authentication, BCrypt, Spring Security |
⚡ Real-Time Stock Trading Engine
Production-grade stock trading platform with live order matching similar to Zerodha and NSE. Built with an order matching algorithm using priority queues, real-time WebSocket price feeds, and asynchronous trade execution via Apache Kafka.
| Attribute | Details |
|---|---|
| Stack | Java, Spring Boot, Apache Kafka, WebSocket, PostgreSQL, Docker, JWT, Spring Cloud |
| Architecture | 6 independent microservices with Eureka service discovery |
| Matching Algorithm | Max heap for buy orders + Min heap for sell orders |
| Real-Time Feed | 10 Indian stocks streamed every 2 seconds via WebSocket STOMP |
| Trade Processing | Sub-second order matching with partial fill support |
| Security | JWT authentication across all endpoints via API Gateway |
| Repository | Trading Engine |
How It Works:
- Order Service receives buy/sell orders and publishes events to Kafka topic
order-events - Matching Engine consumes events and uses two priority queues per stock symbol to find price matches
- When best bid ≥ best ask, trade executes — partial fills handled automatically with remaining quantity re-queued
- Trade Service consumes
trade-eventstopic and asynchronously updates buyer/seller balances and trade history - Market Data Service generates realistic price movements for 10 Indian stocks and broadcasts via WebSocket every 2 seconds
🛡️ API Rate Limiter as a Service
Distributed rate limiting platform similar to Kong and AWS API Gateway. Developers register APIs and configure custom rate limit rules. The platform enforces limits using Redis and streams every request event to Kafka for real-time analytics.
| Attribute | Details |
|---|---|
| Stack | Java, Spring Boot, Redis, Apache Kafka, PostgreSQL, Docker, JWT, Spring Cloud |
| Architecture | 5 independent microservices with Eureka service discovery |
| Algorithms | Fixed Window, Sliding Window, Token Bucket, Leaky Bucket |
| Storage | Redis for sub-millisecond counter lookups per client per API |
| Analytics | Kafka pipeline tracking total, allowed, and blocked requests |
| Security | JWT-secured endpoints; HTTP 429 on limit breach |
| Repository | API Rate Limiter |
How It Works:
- Developers register APIs via API Config Service and receive a unique API key with configurable rate limit rules
- Every request hits the Rate Limiter Service which fetches config via Feign and checks Redis counters
- Redis stores request counts with TTL — Fixed Window resets counter, Sliding Window uses sorted sets with timestamps
- Token Bucket refills tokens based on elapsed time; Leaky Bucket drains queue at fixed rate
- Every check publishes an event to Kafka consumed by Analytics Service, storing request metrics per API key
🍔 Food Delivery Microservices Backend
Swiggy-like food delivery backend with a complete order and payment lifecycle. Features JWT-secured endpoints, OpenFeign inter-service communication, and automatic order status transitions on payment and refund.
| Attribute | Details |
|---|---|
| Stack | Java, Spring Boot, Spring Cloud, PostgreSQL, Supabase, JWT, BCrypt, Docker |
| Architecture | 6 domain-driven microservices with Spring Cloud Gateway |
| Security | JWT + BCrypt (cost factor 10); 100% request interception at Gateway |
| Communication | OpenFeign for synchronous inter-service REST calls |
| Payment Flow | 2 automatic transitions: PENDING → CONFIRMED → CANCELLED on refund |
| Endpoints | 20+ REST endpoints across all services |
| Repository | Food Delivery App |
How It Works:
- User Service handles registration with BCrypt password hashing and JWT token generation on login
- API Gateway validates JWT on every request before routing to downstream services
- Order Service calls Restaurant Service via Feign to fetch real-time menu item prices for order total calculation
- Payment Service processes payments and calls Order Service via Feign to update status to CONFIRMED
- On refund, Payment Service updates status to REFUNDED and order status transitions to CANCELLED automatically
| Institution | Degree | Duration | CGPA |
|---|---|---|---|
| Vellore Institute of Technology, Amaravati | B.Tech Computer Science & Engineering | 2023 – 2027 | 8.21 / 10.0 |
| Recognition | Details |
|---|---|
| Production-Grade Projects | Built 3 enterprise-level backend systems independently |
| Distributed Systems | Implemented 4 rate limiting algorithms used by industry leaders like AWS |
| Order Matching Engine | Built real-time trading system with priority queue-based matching algorithm |
| Event-Driven Architecture | Integrated Apache Kafka across multiple projects for async event streaming |
| WebSocket Integration | Implemented real-time data feeds with STOMP protocol |
current_focus:
learning:
- System Design patterns for large-scale distributed systems
- Advanced Kafka streams and event sourcing
- Kubernetes for container orchestration
building:
- Real-Time Stock Trading Engine
- API Rate Limiter as a Service
- Personal portfolio website
exploring:
- gRPC for high-performance inter-service communication
- CQRS and Event Sourcing patterns
- Cloud-native deployment on AWS
open_to:
- Backend Engineering Internships
- Distributed Systems Projects
- Open Source Contributions
- Software Engineering Full-Time Roles (2027)
"The best systems are not built once — they are designed to evolve, scale, and survive."