Lead Java Engineer AI Native

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.

• 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 teams 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

• 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 teams 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)

• 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