Java_AI Native Senior Java Engineer

EPAM Systems

AI Native Senior Java Engineer

AI-first. Agentic. T-shaped. A specialist who owns the full stack.

Level

Senior Engineer (Level 3 EPAM Global Competency Framework)

Experience

4–10 years of professional experience

Location

India (Hybrid) — multiple cities

Employment

Full-Time

ABOUT THE ROLE

EPAM is hiring AI Native Senior Java Engineers to join our India delivery teams on large-scale enterprise engagements across BFSI, Retail, Healthcare, and Technology verticals. This is not a role for engineers who experiment with AI on the side. At EPAM, AI Native means you build software with AI as a first-class capability: you use frontier LLM models and coding assistants across every phase of the SDLC, design and deploy agentic pipelines, build Model Context Protocol (MCP) servers to connect those pipelines to real enterprise systems, and adapt as the frontier moves. You are a Polyglot Agentic Engineer — T-shaped, outcome-driven, and genuinely curious about what the latest model release changes about how you work.

What makes this role AI Native:

Uses frontier AI models (Claude, GPT-4o, Gemini) and coding assistants (GitHub Copilot, Cursor, Claude Code) daily — across coding, testing, review, and documentation

Has built and deployed at least one MCP server that exposes tools or data to an LLM agent

Has designed or implemented an end-to-end agentic pipeline that connects multiple tools/systems via AI agents

Integrates agentic systems with enterprise tools (Jira, GitHub, databases, monitoring) via MCP or REST/event APIs

Tracks frontier LLM releases and agent framework evolution; adapts practices within weeks, not quarters

KEY 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

Design and implement end-to-end agentic SDLC pipelines: 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 (Jira, Confluence, GitHub, ServiceNow, observability stacks) via MCP connectors or REST/event-driven APIs

Use AI coding assistants (GitHub Copilot, Cursor, Claude Code, or equivalent) and frontier LLMs (Claude, GPT-4o, Gemini) across the full development lifecycle every day; critically evaluate AI outputs for correctness, security, and edge cases before committing

Bring an AI-first mindset: automate repetitive engineering tasks, measure outcomes rather than activity, and identify AI-leverage opportunities within your delivery area

Contribute to the team’s shared library of prompt templates, reusable agent patterns, and MCP connectors

Conduct code and architecture reviews; mentor Junior and Mid-level engineers in Java best practices and AI-native engineering methods

Maintain strong automated test coverage (unit, integration, contract, AI-generated) and healthy CI/CD pipeline practices

Actively track frontier developments — new model releases (Claude, GPT, Gemini, Llama), emerging agent frameworks, new MCP connectors — and bring relevant changes back to the team within weeks

MUST-HAVE REQUIREMENTS

Java Engineering

4–10 years of hands-on Java development in production environments

Strong proficiency in Spring Boot, Spring MVC, Spring Security, and RESTful API design

Solid experience with microservices and event-driven patterns (Kafka, RabbitMQ, or similar)

Cloud platform experience — AWS, GCP, or Azure — including containerization (Docker, Kubernetes)

Working knowledge of relational (PostgreSQL, MySQL) and NoSQL (MongoDB, Redis) databases

CI/CD pipelines (Jenkins, GitHub Actions, GitLab CI) and DevOps engineering practices

AI Native Capabilities

Active daily use of AI coding assistants (GitHub Copilot, Cursor, Claude Code, or equivalent) and frontier LLMs — fluent, not experimental

Hands-on experience building and deploying at least one MCP server (exposing APIs, tools, or data sources to an LLM agent)

Demonstrated experience designing or implementing an agentic workflow or pipeline that connects multiple tools or services via LLM-orchestrated …