🚀 Backend Developer passionate about building clean, high-performance, and scalable systems.

• 💼 Currently engineering solutions at Tata Nexarc.
• 🚀 Founder of MatchSolver.com, an AI-powered career growth platform.
• 🛠️ Specializing in Java, Spring Boot, Microservices, and System Design.
• 💳 Experienced in Payment Gateways, Transactions, and OCR Invoice Systems.
• 🎮 Casual gamer & massive fan of the GTA franchise.
• 🇮🇳 Based in Andhra Pradesh, India.

AI-Powered Career Optimization & Document Management Platform

• 🤖 AI Career Intelligence: Integrates resume building, cover letter optimization, ATS screening, and mock interview preparation.
• ⚙️ 50+ Automated Utilities: Instant online processing suite for PDF, image, and text document formatting.
• ⚡ Production-Grade Architecture: Engineered for high concurrency, low latency file parsing, and secure payment processing.

• Resume Parsing Engine: Utilizes custom optical character recognition (OCR) and text-processing pipelines to extract structured sections from uploaded PDFs and Word files.
• AI Match Optimizer: Evaluates job descriptions alongside user resumes to calculate ATS match rates, highlighting keyword gaps, formatting issues, and offering action-oriented optimizations.
• Scale & Throughput: Designed to process large file uploads asynchronously using thread pools, optimizing resource utilization and minimizing user response time.

As a backend developer at Tata Nexarc, I focus on building reliable transactional features and processing document data:

1. OCR Invoice Systems:
   • Engineered pipelines to parse, extract, and structure invoice information using OCR utilities, reducing manual invoice indexing overhead.
   • Built validation layers to ensure parsed transactional data maps cleanly to double-entry ledger structures.
2. Transaction & Payment Gateways:
   • Integrated reliable checkout pipelines with major payment gateways, handling webhook processing, automatic transaction retries, and reconciliation routines.
   • Implemented distributed locking (via Redis) to prevent double-spending and ensure transaction consistency under concurrent request spikes.

Here is a high-level representation of the backend workflows and API architectures I design and build:

graph TD
    %% Styling
    classDef default fill:#1a1b26,stroke:#7aa2f7,stroke-width:2px,color:#a9b1d6;
    classDef gateway fill:#ff9e64,stroke:#f7768e,stroke-width:2px,color:#1a1b26,font-weight:bold;
    classDef service fill:#2ac3de,stroke:#0db9d7,stroke-width:2px,color:#1a1b26,font-weight:bold;
    classDef db fill:#9ece6a,stroke:#73daca,stroke-width:2px,color:#1a1b26;

    %% Nodes
    Client["📱 Client Apps / Tata Nexarc / MatchSolver"]
    Gateway["🛡️ API Gateway (Spring Cloud / Security)"]:::gateway
    
    subgraph Microservices ["Core Backend Layer"]
        Auth["🔑 Auth Service (OAuth2 / JWT)"]:::service
        Invoice["📄 OCR Invoice Processing Service"]:::service
        Payment["💳 Payment Gateway & Transactions"]:::service
    end

    subgraph DataStore ["Data & Event Streaming"]
        DB[("🗄️ PostgreSQL / MySQL / Hibernate")]:::db
        Cache[("⚡ Redis Cache")]:::db
        Queue[("✉️ RabbitMQ Message Broker")]:::db
    end

    %% Flows
    Client -->|REST APIs / HTTPS| Gateway
    Gateway --> Auth
    Gateway --> Invoice
    Gateway --> Payment

    Invoice -->|Async Jobs| Queue
    Payment -->|Transaction Events| Queue
    Queue -->|Database Persistence| DB
    Auth -->|Session Cache| Cache

• High Availability & Fault Tolerance: Implementing Circuit Breakers (Resilience4j) and fallback mechanisms to keep downstream outages from propagating.
• Database Optimization: Designing normalized schemas, writing indexed queries, optimizing JPA/Hibernate mapping relationships to avoid the $N+1$ query problem, and configuring connection pools (HikariCP).
• Caching & Session Routing: Leveraging Redis for fast read-through and write-behind cache strategies, API rate-limiting, and managing shared user sessions across distributed instances.
• Messaging & Event-Driven Architecture: Decoupling long-running operations (like AI-processing and OCR conversions) from direct HTTP request threads using RabbitMQ queues to guarantee reliable job execution.

Backend & Databases	Frontend & UI	DevOps & Tools

• SOLID Design: Strictly adhering to SOLID principles and Clean Code rules to keep codebases understandable and modular.
• API Standardization: Structuring restful APIs with clean HTTP status mappings, custom exception handlers (@ControllerAdvice), and comprehensive Swagger/OpenAPI documentation.
• Testing Guidelines: Writing comprehensive unit and integration tests using JUnit, Mockito, and Testcontainers to validate behavior across database layers.

Below are the core architectural patterns I implement to maintain system decoupling and performance:

• CQRS (Command Query Responsibility Segregation): Segregating read and write operations using separate database models (like PostgreSQL for transactional writes and Redis/ElasticSearch for optimized reads).
• Transactional Outbox Pattern: Assuring reliable message publishing to RabbitMQ or Kafka in distributed microservices transactions by using an outbox table within the same relational database boundary.
• Distributed Locking (Redis/Redlock): Implementing distributed lock mechanisms to manage concurrency and prevent race conditions on payment captures and inventory deductions.

I believe that a backend system is only as good as its observability:

• Observability: Setting up distributed tracing with Spring Cloud Sleuth (Micrometer) and Zipkin/Jaeger to track user requests across microservice boundaries.
• Log Aggregation & Monitoring: Working with Prometheus and Grafana dashboards to monitor JVM health metrics, thread pools, and active DB connection parameters.
• CI/CD Pipelines: Constructing automated Jenkins/GitHub Actions pipelines to build Docker images, execute unit/integration test suites, and deploy to AWS Elastic Container Service (ECS).

• 🧠 Delving deeper into System Architecture Patterns and high-throughput low-latency network protocols (gRPC, WebSockets).
• 🔧 Researching distributed consensus algorithms (Raft, Paxos) and cloud-native container orchestration using Kubernetes.
• 🤖 Experimenting with the integration of AI models and OCR libraries to automate complex business workflows.

🔥 3D Contribution Graph