Rajeev Nayan

Java Backend Engineer | Spring Boot | Microservices | AI Engineering

About • What I Build • Tech Stack • Featured Projects • AI Engineering • System Design • Coding Journey • GitHub Stats • Connect

I am a Java Backend Engineer focusing on building scalable, reliable, and secure server-side systems. I design robust RESTful APIs, orchestrate Microservices, and optimize databases. Currently, I am also exploring AI Integration in backend architectures—building Retrieval-Augmented Generation (RAG) systems and leveraging LLMs using Spring AI.

• 🔭 Current Focus: Architecting fault-tolerant microservices and asynchronous task pipelines.
• 🌱 Learning & Exploration: Caching topologies (Redis clustering), advanced system security, and Spring AI agents.
• 💬 Ask me about: Transaction boundaries, JPA query optimization, thread safety, and API design.

I focus my development practices around three distinct pillars:

Building robust backend architectures with Java and Spring Boot. I specialize in designing transactional APIs, securing endpoints via Spring Security (JWT & OAuth2), and implementing data persistence layers using Spring Data JPA & Hibernate.

Constructing systems that scale beyond a single instance. I design RESTful systems, handle asynchronous processing via messaging layers (Kafka, RabbitMQ), implement distributed caching strategies with Redis, and manage connection pools.

Integrating LLMs and semantic indexing directly into backend systems. I build Retrieval-Augmented Generation (RAG) structures using Spring AI, handling vector database integrations, similarity searches, and custom prompt formatting natively in Java.

A complete ticket booking system backend engineered to configure seat layouts, list showtimes, and handle high-volume ticket bookings.

• Tech: Java • Spring Boot • Spring Security (JWT) • MySQL • JPA & Hibernate
• Engineering: MVC Architecture • Entity Relationship Design (OneToMany/ManyToOne) • Transaction Boundaries (@Transactional) • Secure REST Endpoints
• Repository: View Project →

An online food ordering system backend processing menus, categories, persistent shopping carts, and order status lifecycles.

• Tech: Java • Spring Boot • Hibernate • MySQL • Thymeleaf
• Engineering: Form Validation • Persistent Cart State Management • Asynchronous Lifecycle Updates
• Repository: View Project →

Rental listing and application marketplace allowing secure logins for landlords and tenants.

• Tech: React • JavaScript • JWT Authentication • Google OAuth 2.0 • RESTful APIs
• Engineering: Front-to-Back decoupled API communications • Token-based stateless authentication sessions
• Repository: View Project →

An AI-powered command-line utility designed to read repository file trees and automatically generate clean markdown files.

• Tech: Node.js • LLM APIs • Prompt Engineering
• Engineering: Recursive directory parsing • Context window formatting for LLM queries
• Repository: View Project →

I build semantic retrieval flows that connect enterprise databases and document sets safely to Language Models:

[ Raw Documents ] ──> [ Chunking / Parsing ] ──> [ Embedding Model ] ──> [ Vector Store (Pgvector/Redis) ]
                                                                                   │
                                                                                   ▼ (Semantic Search)
[ User Query ] ─────────────────────────> [ Context Formatter ] ────────> [ LLM Generation ] ──> [ Output ]

• Spring AI Integration: Working with Java-native APIs to construct vector database stores, configure similarity search query parameters, and handle LLM tokens without a Python container.
• Semantic Retrieval: Structuring metadata mappings inside Vector search indices to ensure accurate system context limits.

I build applications with structural patterns in mind to keep them maintainable as their scale requirements expand:

• Layered Architecture: Decoupling the Presentation layer (REST Controllers), Business logic (Service interfaces), and Persistence layer (Spring Repositories).
• Asynchronous Offloading: Offloading email confirmations, logging pipelines, and report generation from the HTTP request thread into messaging queues (RabbitMQ/Kafka).
• Distributed Caching: Placing Read-through Redis caching strategies ahead of relational queries to optimize read-heavy endpoints.

I practice algorithmic problem solving daily on LeetCode and GeeksforGeeks.

• Profile: View LeetCode Profile →
• Platform Stats: Dynamically updated stats via API queries:

• Profile: View GeeksforGeeks Profile →
• Platform Stats: Dynamically parsed GfG profile scores:

• Profile: View Codolio Profile →
• Platform Stats: Comprehensive developer portfolio statistics:

• Readable > Clever: Code is read much more often than it is written.
• Observable > Silent: Always include telemetry, logs, and trace bounds for execution paths.
• Scalable > Prematurely Optimized: Benchmark constraints before building premature topologies.

If you'd like to talk about Spring Boot setups, Java performance tuning, vector databases, or backend architectures:

• 📧 Email: imrajeevnayan@gmail.com
• 💼 LinkedIn: linkedin.com/in/imrajeevnayan
• 🌐 Portfolio: rajeevnayan.in