Lead Java Engineer - AI Native
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Key skills for this role
Role Overview
Lead Java Engineer – AI Native role focused on scaling enterprise Java systems and pioneering AI-native engineering practices across the SDLC.
The role combines Java architecture expertise with hands-on agentic pipelines and MCP server ecosystems connecting enterprise systems to LLM-based agents.
The role requires working from the office three days per week and mentoring engineering teams while driving AI adoption.
Key Skills for This Role
Full Job Posting
Role overview
Lead Java Engineer – AI Native role focused on scaling enterprise Java systems and pioneering AI-native engineering practices across the SDLC.
The role combines Java architecture expertise with hands-on agentic pipelines and MCP server ecosystems connecting enterprise systems to LLM-based agents.
The role requires working from the office three days per week and mentoring engineering teams while driving AI adoption.
Responsibilities
- Design and maintain scalable Spring Boot and microservices applications with end-to-end feature ownership.
- Build and deploy MCP servers exposing Java services, databases, and internal tools to LLM-based agents.
- Architect agentic SDLC pipelines covering specification, code generation, testing, CI/CD, and deployment validation.
- Integrate pipelines with Jira, Confluence, GitHub, ServiceNow, and observability platforms through MCP or APIs.
- Use AI coding assistants and frontier LLMs daily while evaluating outputs for correctness, security, and edge cases.
- Conduct reviews, mentor engineers, maintain automated test coverage, and monitor emerging AI and MCP developments.
Requirements
- 8–12 years of professional Java development experience owning complex production systems.
- Expertise in Spring Boot, Spring Cloud, Spring Data, Spring Security, and microservices design patterns.
- Understanding of distributed systems, event-driven architecture, DDD, and CQRS/event sourcing.
- Cloud-native engineering experience on AWS, GCP, or Azure, including IaC, serverless, and managed services.
- Experience leading technical teams and coaching 8–15 engineers in AI-native practices.
- Hands-on MCP ecosystem and production agentic SDLC pipeline experience, including security, versioning, observability, and API integration.
- Upper-Intermediate English proficiency or above, equivalent to B2+.
Preferred skills
- Experience with RAG pipelines, LLM fine-tuning, or evaluation frameworks such as RAGAS and DeepEval.
- Familiarity with structured agentic SDLC methods, managed services or AIOps delivery, and function-calling design.
- Contributions to AI maturity assessments, team certification programs, or AI engineering playbooks.
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