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. Youll 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:

37 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:

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  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).