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).