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 (