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
Note: If you are matching on to the above job description & skills, please feel free to fill in your details on the below Form enclosed (https://forms.gle/bUyXMBhgSxxBZVeK9).