Senior Java Developer

Epam Systems; Pune District, Bengaluru, Hyderabad; On-site; INR 1,800,000 - 3,200,000; Full time

Benefits offered by this job

Hybrid work model

Enterprise‑scale Java projects

AI upskilling and career progression

Job summary

EPAM Systems is hiring an AI Native Senior Java Engineer to join our India delivery teams on large-scale enterprise engagements across BFSI, Retail, Healthcare, and Technology verticals. You will build software with AI as a first-class capability, using frontier models and tooling across the SDLC, and deploy agentic pipelines connected to enterprise systems.

The role emphasizes end‑to‑end ownership, a polyglot agentic approach, and collaboration with teams to deliver scalable Java applications,

Qualifications

• Hands-on Java development with Spring Boot and microservices.
• Cloud experience (AWS/GCP/Azure) including containers.
• Relational and NoSQL databases knowledge; CI/CD pipelines.

Responsibilities

• Design, develop, and maintain scalable Java applications using Spring Boot; own features end‑to‑end.
• Build and deploy MCP servers exposing services to LLM agents.
• Design end-to-end AI-native SDLC pipelines orchestrated by AI agents.
• Integrate pipelines with Jira/Confluence/GitHub/ServiceNow via MCP connectors or REST APIs.
• Mentor engineers and maintain strong automated test coverage.

Skills

Java development

Spring Boot

Microservices

Cloud platforms

Databases

CI/CD

English (B2+)

Tools

Docker

Kubernetes

Kafka

RabbitMQ

GitHub Actions

Job description

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

5–10 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 agents
• Ability to integrate agentic pipelines with enterprise systems via MCP or REST/event APIs — must have built it, not just read about it
• Working knowledge of at least one agent orchestration framework: LangChain, LangGraph, CrewAI, AutoGen, or Spring AI Agents
• Strong critical evaluation of AI‑generated code: able to identify correctness issues, security gaps, and performance problems in AI outputs
• Genuine learning agility: can describe how your engineering practice changed meaningfully in the last 6–12 months due to new AI tools or model capabilities
• English proficiency: Upper-Intermediate or above (B2+)

NICE TO HAVE

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

WHAT WE OFFER

• Enterprise‑scale Java projects for global Fortune 500 clients — real complexity, real ownership
• Work at the frontier of AI‑native software delivery: agentic pipelines, MCP ecosystems, and autonomous SDLC automation — in production, not in a lab
• Access to a broad enterprise AI toolchain and an internal AI enablement community to accelerate your growth
• Structured AI upskilling: training, certifications, and a clear AI maturity progression path aligned to EPAM's global framework
• Competitive compensation, hybrid working model, and EPAM Learning (50,000+ courses globally)
• Clear career path: Senior Lead Principal Engineer, supported by EPAM's Global Competency Framework

EPAM Systems is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.

Learn more at www.epam.com/careers