Senior Java Developer with GenAI (Java, Spring Boot, LLM, LangGraph, RAG) - Immediate Joiners

Role Overview:

Senior Java Backend Engineer

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