Sr. Java Developer with GenAI Integration (Java, LLM, RAG) - Immediate Joiners

Role Overview :

We are looking for a skilled Senior Java Backend Engineer to join our team focused on building scalable and high-performance backend systems for Generative AI applications. You’ll play a key role in designing APIs, orchestrating AI agents, and integrating Large Language Models (LLMs) into production-ready systems. This role is ideal for backend developers with a passion for modern AI technologies and distributed systems.

Key Responsibilities :

Design, build, and maintain scalable backend services using Java and the Spring Boot framework

Develop APIs to enable LLM integration and support AI agent orchestration workflows

Architect microservices to power RAG (Retrieval-Augmented Generation) and other LLM-driven systems

Optimize performance through efficient caching strategies and vector database interactions

Integrate and manage connections with multiple LLM providers (e.g., OpenAI, Gemini, Claude), including rate limiting and failover handling

Implement real-time streaming features for conversational AI systems using WebSockets or similar technologies

Ensure robust system observability with logging , monitoring , and tracing

Required Skills & Qualifications :

3–7 years total, with a minimum of 1 year working on Generative AI projects

Backend Development :

Minimum 3 years of hands-on experience with Java and Spring Boot

Strong grasp of RESTful API design principles and microservices architecture

Proficiency with core Spring modules : Spring Security , Spring Data JPA , Spring Cloud

Experience working with relational and NoSQL databases : PostgreSQL , MongoDB , Redis

Familiarity with message brokers such as RabbitMQ or Apache Kafka

Expertise in caching mechanisms and system performance tuning

Generative AI Integration :

Experience integrating LLM APIs (OpenAI, Gemini, Claude, etc.) into backend services

Knowledge of vector databases and semantic search technologies

Familiarity with AI agent orchestration frameworks (e.g., LangGraph )

Understanding of RAG systems and how to implement them effectively

Experience developing streaming responses using WebSockets or server-sent events

Working knowledge of prompt templating and management systems

Nice to Have :

Experience in fine-tuning LLMs and managing model deployment pipelines

Knowledge of self-hosted LLM environments and infrastructure management

Exposure to observability tools like LangSmith or custom monitoring setups

Familiarity with natural language to SQL systems or BI applications powered by LLMs

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