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Java / AI Developer
Kumaran Systems —Chennai, Tamil Nadu
- Full-time
- Azure
- Windows
- Java

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Java / AI Developer
Kumaran Systems —Chennai, Tamil Nadu
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Java / AI Developer

Kumaran Systems -
Chennai, Tamil Nadu

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Job details

Full-time

Qualifications

Spring Boot
CI/CD
Azure
Gradle
Law
Kubernetes
Configuration management
Windows
Java
Master's degree
Databases
Microservices
AWS
Docker
Continuous integration
JPA
REST
APIs
Budgeting
Maven
AI
Spring framework
Hibernate
Design patterns

Full job description

Job Information

Department Name

Banking

Date Opened

03/31/2026

Industry

IT Services

Job Type

Full time

Work Experience

5+ years

City

Chennai

State/Province

Tamil Nadu

Country

India

Zip/Postal Code

600032

About Us

Kumaran Systems: Engineering Future-Ready Transformations Since 1992, Kumaran Systems has been helping enterprises bridge the past and the future, reimagining legacy systems and embracing AI-led possibilities. With over 34 years of delivery excellence, we specialise in re-engineering core applications, driving cloud transformation, and building automation-first, GenAI-enabled ecosystems across the Banking and Financial Services, Insurance, Telecom, and Automotive sectors. We help organisations not just upgrade their systems but reimagine what’s possible.

Why Kumaran?

We blend engineering discipline with AI innovation, helping clients modernise with confidence, automate with clarity, and scale with purpose. Our global delivery model ensures agility, responsiveness, and seamless collaboration, with clients always at the heart of every engagement. At Kumaran, we don’t just solve problems, we engineer future-ready transformations.

Job Description

Job Description: Java Developer – AI Integration (Mid-Level)

Role Overview

We are looking for a Mid-Level Java Developer (4-9 years) with hands-on experience in AI integration to join our product engineering team. In this role, you will be responsible for embedding AI capabilities - including converting the user-provided natural language inputs, or database metadata information into valid product configurations, Retrieval-Augmented Generation (RAG), LLM-powered features, and agentic workflows - directly into our Java/Spring Boot-based enterprise product. You will work at the intersection of traditional backend engineering and modern AI/LLM technologies, owning the design, development, and maintenance of AI-powered components end-to-end.

Key Responsibilities:

AI Feature Development

Capture user intent via a conversational/chat interface and translate it into valid product configurations.
Extract metadata from heterogeneous sources - file formats (CSV, Excel, PDF), relational database schemas, and API contracts (OpenAPI, WSDL) and convert them to valid product configurations.
Design and implement RAG pipelines that retrieve contextually relevant information from internal knowledge bases and surface it through LLM-generated responses.
Integrate LLM-powered features such as intelligent chat, document summarization, content generation, and semantic search into the existing product.
Build and maintain AI agent workflows - including tool use, multi-step reasoning chains, and orchestrated LLM calls - to automate complex product-level tasks.

Author, version, and iterate on system prompts and user prompt templates to reliably steer LLM behavior.
Apply advanced prompting techniques: chain-of-thought, few-shot examples, output format enforcement, and instruction following.

Integrate with multiple LLM providers - OpenAI/Azure OpenAI, Anthropic Claude, AWS Bedrock, and open-source models (LLaMA, Mistral, etc.) - through their APIs.
Build provider-agnostic abstraction layers using Spring AI and LangChain4j to allow model switching without core code changes.

Backend Engineering

Write clean, maintainable, production-grade Java code following SOLID principles and enterprise design patterns.
Integrate AI services with existing relational databases via JPA/Hibernate, including schema design for storing conversation history, embedding metadata, and audit trails.
Manage build pipelines and dependency governance using Maven or Gradle.

Testing & Quality

Write comprehensive unit, integration, and contract tests for AI-integrated components.
Handle non-deterministic LLM output gracefully - implement fallback logic, retry strategies, and output validation.
Monitor AI feature performance in production; instrument logging and tracing for LLM calls, including token usage, latency, and error rates.

Mandatory Skills & Experience

Java & Backend

4–9 years of professional Java development experience.
Strong proficiency in Spring Boot - REST controllers, service layer design, dependency injection, and configuration management.
Experience building and consuming RESTful Microservices.
Working knowledge of JPA/Hibernate - entity modelling, JPQL, transaction management.

AI / LLM Engineering

Hands-on experience integrating with at least one major LLM provider API (OpenAI, Azure OpenAI, Anthropic, or AWS Bedrock) from Java.
Practical experience with Prompt Engineering - not just calling APIs, but crafting, testing, and iterating prompts to achieve reliable, structured outputs.
Experience building RAG pipelines - document ingestion, chunking strategies, embedding generation, similarity retrieval, and context injection into prompts.
Working knowledge of LangChain4j and/or Spring AI frameworks for LLM orchestration within Java applications.
Understanding of AI agent concepts - tool/function calling, agent loops, multi-step task orchestration.

Good-to-Have Skills

Experience with vector databases such as Pinecone, Weaviate, Qdrant, pgvector, or Chroma for embedding storage and semantic search.
Exposure to multiple LLM providers (OpenAI, Anthropic Claude, AWS Bedrock, LLaMA/Mistral via Ollama or similar).
Familiarity with open-source LLM deployment - running models locally or on self-hosted infrastructure.
Knowledge of token budgeting, context window management, and cost optimization strategies for LLM APIs.
Experience with observability tooling for AI systems - LangSmith, Helicone, or custom logging pipelines.
Familiarity with streaming responses from LLMs (Server-Sent Events / WebSocket delivery to frontend).
Understanding of embedding models - how they differ from generative models, when to use which, and how to evaluate embedding quality.
Exposure to CI/CD pipelines and containerization (Docker, Kubernetes).

Kumaran Systems is an Equal Opportunity Employer and does not discriminate on the basis of race or ethnicity, religion, sex, national origin, age, veteran disability or genetic information or any other reason prohibited by law in employment.

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