Rajeev Nayan

Java Backend Engineer | Spring Boot | Microservices | AI Engineering

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I am a Java Backend Engineer focused on building scalable, reliable, and secure server-side systems. I design robust RESTful APIs, orchestrate Microservices, and optimize relational and distributed databases. Additionally, I build AI-integrated backends—leveraging Retrieval-Augmented Generation (RAG) and LLM agents with Spring AI.

• 🔭 Focus: Architecting fault-tolerant microservices, transaction boundaries, and asynchronous event pipelines.
• 🌱 Exploring: Distributed caching topologies (Redis clustering), advanced system security, and Spring AI agents.
• 💬 Ask me about: Java concurrency, JPA query optimization, thread safety, and API design.

Ticket booking platform 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: Layered Architecture • Relational Entity Mapping • Transaction Boundaries (@Transactional) • Secure REST Endpoints
• Repository: View Project →

Online food ordering backend processing menus, categories, persistent shopping carts, and order lifecycles.

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

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

• Tech: React • JavaScript • JWT Authentication • Google OAuth 2.0 • RESTful APIs
• Engineering: Decoupled Client-Server API Communication • Stateless Token Authentication
• Repository: View Project →

AI-powered CLI utility designed to parse repository structures and generate clean markdown documentation.

• Tech: Node.js • LLM APIs • Prompt Engineering
• Engineering: Recursive Directory Traversal • Context Window Structuring for LLM Analysis
• 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 scale requirements expand:

• Layered Architecture: Decoupling the Presentation layer (REST Controllers), Business logic (Service interfaces), and Persistence layer (Spring Repositories).
• Asynchronous Offloading: Offloading notification triggers, 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 across LeetCode, GeeksforGeeks, and Codolio.

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

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