Lead Java Engineer - AI Native

EPAM Systems; Hyderabad; Hybrid; INR 300,000 - 540,000; Full time

Job summary

EPAM Systems in Hyderabad seeks a Lead Java Engineer – AI Native to design and scale enterprise Java systems, building agentic pipelines and MCP server ecosystems that connect to LLM-based agents.

You will mentor teams, drive AI adoption at scale, and own end-to-end features using Spring Boot, microservices, and cloud platforms.

Qualifications

• 8–12 years of professional Java development experience with ownership of complex production systems.
• Expertise in Spring Boot, Spring Cloud and Spring Data with Spring Security.
• Cloud-native engineering on AWS, GCP or Azure including IaC and serverless patterns.
• Experience mentoring technical teams and leading architectural governance.
• Daily hands-on use of AI coding assistants and frontier LLMs.

Responsibilities

• Design, develop and maintain scalable Java applications using Spring Boot and microservices, owning features end-to-end with autonomy.
• Build and deploy MCP servers to expose Java services and tools to LLM-based agents.
• Architect end-to-end agentic SDLC pipelines with AI-driven code generation and automated testing.
• Integrate agentic pipelines with Jira, Confluence, GitHub and ServiceNow via MCP connectors or APIs.
• Apply AI coding assistants and evaluate AI outputs for correctness and security.
• Automate repetitive tasks and measure outcomes to improve engineering efficiency.
• Contribute to prompt templates, reusable agent patterns and MCP connectors.
• Mentor Junior and Mid-level engineers in Java best practices and AI-native methods.
• Maintain strong automated test coverage and CI/CD practices.
• Track frontier model releases and integrate relevant changes.

Skills

Java

Spring Boot

Microservices

AI-native engineering

Mentoring

Team leadership

English B2+

GitHub Copilot

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Claude Code

Tools

MCP

GitHub Copilot

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Claude Code

Job description

We are looking for a Lead Java Engineer – AI Native to design and scale enterprise Java systems while pioneering AI-native engineering practices across the SDLC. This role combines deep Java architecture expertise with hands‑on experience building agentic pipelines and MCP server ecosystems that connect enterprise systems to LLM‑based agents.

The role requires 3 days a week working from the office and involves mentoring engineering teams while driving AI adoption at scale.

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 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, ServiceNow and observability stacks via MCP connectors or REST/event‑driven APIs
• Apply AI coding assistants and frontier LLMs across the full development lifecycle daily and critically evaluate AI outputs for correctness, security and edge cases before committing
• 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

• 8–12 years of professional Java development experience with clear ownership of complex production systems
• Expertise in Spring Boot, Spring Cloud and Spring Data along with Spring Security and microservices design patterns
• Understanding of distributed systems, event‑driven architecture and domain‑driven design (DDD) plus CQRS/ES
• Proficiency in cloud‑native engineering on AWS, GCP or Azure including IaC, serverless patterns and managed services
• Background in leading technical teams across architecture governance, coding standards and mentoring
• Daily hands‑on proficiency in AI coding assistants such as GitHub Copilot, Cursor and Claude Code and frontier LLMs including Claude, GPT‑4o and Gemini, with capability to coach a team of 8‑15 engineers in AI‑native practices
• Hands‑on expertise in designing, building and deploying MCP server ecosystems at project or account scale including security controls, versioning and observability
• Capability to architect and operate end‑to‑end agentic SDLC pipelines integrated with enterprise tools via MCP and APIs in production environments
• Skills in evaluating and selecting AI agent orchestration frameworks such as LangGraph, CrewAI and AutoGen or Spring AI Agents for production use with documented rationale and trade‑offs
• Showcase of improving a team's AI maturity supported by adoption metrics or productivity evidence
• Demonstrated learning agility at team scale with evidence of driving meaningful changes to engineering practices in the last 12 months due to evolving frontier models and tools
• English proficiency at Upper-Intermediate level or above (B2+)

Nice to have

• Experience with RAG pipelines, LLM fine-tuning or LLM evaluation frameworks such as RAGAS and DeepEval applied to software engineering contexts
• Familiarity with structured agentic SDLC methodologies including specification‑driven AI development and specification hardening or equivalent governed delivery protocols
• Experience with Managed Services or AIOps delivery models such as autonomous monitoring, AI‑assisted incident response and intelligent operations pipelines
• Skills in function calling and tool‑use design across multiple frontier models to build reliable governed tool‑use chains
• Contributions to internal AI maturity assessments, team certification programmes or AI engineering playbooks