Senior Java Engineer - AI Native

EPAM Systems; Coimbatore District; Hybrid; INR 2,400,000 - 3,600,000; Full time

Job summary

EPAM Systems is seeking a Senior Java Engineer - AI Native in Coimbatore to design and deliver scalable Java applications. You will embed AI-native practices across the SDLC and build MCP connectors to enable LLM-based agents with live enterprise data.

Requirements include 5–10 years of Java experience, Spring Boot, microservices, and cloud platforms such as AWS/GCP/Azure. You will mentor engineers and shape automated testing and CI/CD practices within a delivery-oriented team.

Qualifications

• 5-10 years of hands-on Java development experience in production environments.
• Proficiency in Spring Boot, Spring MVC and Spring Security with RESTful API design.
• Experience with microservices and event-driven patterns using Kafka or RabbitMQ.
• Background in cloud platforms such as AWS, GCP or Azure including Docker and Kubernetes containerization.
• Knowledge of relational databases such as PostgreSQL and MySQL alongside NoSQL databases such as MongoDB and Redis.
• Familiarity with CI/CD pipelines including Jenkins, GitHub Actions and GitLab CI and DevOps engineering practices.
• Active daily use of AI coding assistants such as GitHub Copilot, Cursor or Claude Code and frontier LLMs, applied fluently rather than experimentally.
• Hands-on experience building and deploying at least one MCP server that exposes APIs, tools or data sources to an LLM agent.
• Showcase of designing or implementing an agentic workflow or pipeline that connects multiple tools or services via LLM-orchestrated agents.
• Capability to integrate agentic pipelines with enterprise systems via MCP or REST/event APIs through direct hands-on build experience.
• Working knowledge of at least one agent orchestration framework such as LangChain, LangGraph or CrewAI.
• Understanding of how to critically evaluate AI-generated code, identifying correctness issues, security gaps and performance problems, along with genuine learning agility reflected in evolving engineering practice over the last 6-12 months due to new AI tools or model capabilities.
• Upper-Intermediate English proficiency or above (B2+)

Responsibilities

• Design, develop and maintain scalable Java applications using Spring Boot and microservices, owning features end-to-end.
• Build and deploy MCP servers exposing Java services, databases or tools to LLM-based agents.
• Architect end-to-end agentic SDLC pipelines with AI-driven code generation and testing.
• Integrate agentic pipelines with Jira, Confluence, GitHub and ServiceNow via MCP connectors or REST APIs.
• Use AI coding assistants and frontier LLMs across the development lifecycle and evaluate outputs for correctness, security and edge cases.
• Automate repetitive engineering tasks and identify AI-leverage opportunities within delivery.
• Contribute to prompt templates and MCP connectors libraries.
• Mentor Junior and Mid-level engineers in Java and AI-native methods.
• Maintain strong automated test coverage across unit, integration, contract and AI-generated tests.
• Track frontier developments like new model releases and agent frameworks; bring insights back to the team within weeks.

Skills

Java development

Spring Boot

Microservices

Kafka/RabbitMQ

Cloud platforms (AWS/GCP/Azure)

Docker/Kubernetes

REST API design

CI/CD

AI coding assistants

Tools

MCP servers (Model Context Protocol)

Jira/Confluence

GitHub/GitLab CI

LLMs/tools (Copilot, Claude)

Job description

We are looking for a Senior Java Engineer - AI Native to join our team and drive the design and delivery of scalable Java applications while embedding AI-native practices across the full software development lifecycle. This role combines deep Java engineering expertise with hands-on experience building agentic pipelines and MCP integrations that connect enterprise systems to LLM-based agents. The position requires 3 days of work from the office.

Responsibilities

• Design, develop and maintain scalable Java applications using Spring Boot and microservices architecture, owning features end-to-end with a high degree of autonomy
• Build and deploy Model Context Protocol (MCP) servers that expose Java services, databases or internal tools to LLM-based agents, enabling agents to act on live enterprise data and systems
• Architect and implement end-to-end agentic SDLC pipelines including automated specification drafting, AI-driven code generation, intelligent test creation, CI/CD integration and deployment validation orchestrated by AI agents
• Integrate agentic pipelines with enterprise tools and platforms such as Jira, Confluence, GitHub and ServiceNow via MCP connectors or REST/event-driven APIs
• Use AI coding assistants and frontier LLMs across the full development lifecycle daily and critically evaluate AI outputs for correctness, security and edge cases before code commits
• Bring an AI-first mindset to automate repetitive engineering tasks, measure outcomes rather than activity and identify AI-leverage opportunities within the delivery area
• Contribute to the team's shared library of prompt templates, reusable agent patterns and MCP connectors
• Conduct code and architecture reviews and mentor Junior and Mid-level engineers in Java best practices and AI-native engineering methods
• Maintain strong automated test coverage across unit, integration, contract and AI-generated tests along with healthy CI/CD pipeline practices
• Track frontier developments such as new model releases, emerging agent frameworks and new MCP connectors, and bring relevant changes back to the team within weeks

Requirements

• 5-10 years of hands-on Java development experience in production environments
• Proficiency in Spring Boot, Spring MVC and Spring Security with RESTful API design
• Experience with microservices and event-driven patterns using Kafka or RabbitMQ
• Background in cloud platforms such as AWS, GCP or Azure including Docker and Kubernetes containerization
• Knowledge of relational databases such as PostgreSQL and MySQL alongside NoSQL databases such as MongoDB and Redis
• Familiarity with CI/CD pipelines including Jenkins, GitHub Actions and GitLab CI and DevOps engineering practices
• Active daily use of AI coding assistants such as GitHub Copilot, Cursor or Claude Code and frontier LLMs, applied fluently rather than experimentally
• Hands-on experience building and deploying at least one MCP server that exposes APIs, tools or data sources to an LLM agent
• Showcase of designing or implementing an agentic workflow or pipeline that connects multiple tools or services via LLM-orchestrated agents
• Capability to integrate agentic pipelines with enterprise systems via MCP or REST/event APIs through direct hands-on build experience
• Working knowledge of at least one agent orchestration framework such as LangChain, LangGraph or CrewAI
• Understanding of how to critically evaluate AI-generated code, identifying correctness issues, security gaps and performance problems, along with genuine learning agility reflected in evolving engineering practice over the last 6-12 months due to new AI tools or model capabilities
• Upper-Intermediate English proficiency or above (B2+)

Nice to have

• Experience building RAG (Retrieval-Augmented Generation) pipelines covering chunking, embedding and vector stores such as pgvector, Pinecone or Weaviate
• Skills in prompt engineering for development contexts including systematic prompt design, evaluation harnesses and iteration workflows
• Familiarity with LLM evaluation frameworks such as RAGAS or DeepEval to assess agent output quality
• Experience with function calling and tool-use APIs across multiple frontier models from Anthropic, OpenAI and Google
• Exposure to structured agentic SDLC methodologies such as spec-driven development with AI or specification hardening