EPAM Systems
Website:
epam.com
Company:
https://www.linkedin.com/company/epam-systems
Seniority: Mid-Senior level
Industries: Software Development and IT Services and IT Consulting
Job details:
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
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