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EPAM is a leading global provider of digital platform engineering and development services. We are committed to having a positive impact on our customers, our employees, and our communities. We embrace a dynamic and inclusive culture. Here you will collaborate with multi-national teams, contribute to a myriad of innovative projects that deliver the most creative and cutting-edge solutions, and have an opportunity to continuously learn and grow. No matter where you are located, you will join a dedicated, creative, and diverse community that will help you discover your fullest potential. 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 812 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
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.