Senior Java Engineer AI Native

We are seeking a Senior Java Engineer AI Native to design and build scalable Java applications while pioneering AI-driven engineering this role you will own features end-to-end build Model Context Protocol servers and integrate agentic pipelines with enterprise systems using frontier LLMs and AI coding assistants every day to deliver high-quality software.

• 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
• Develop 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
• Leverage AI coding assistants and frontier LLMs across the full development lifecycle critically evaluating AI outputs for correctness security and edge cases before committing
• Apply an AI-first mindset to automate repetitive engineering tasks measure outcomes rather than activity and identify AI-leverage opportunities within your delivery area
• Contribute to the teams shared library of prompt templates reusable agent patterns and MCP connectors
• Conduct code and architecture reviews while mentoring 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 alongside 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

• 510 years of hands-on Java development in production environments
• Proficiency in Spring Boot Spring MVC and Spring Security with RESTful API design
• Experience with microservices and event-driven patterns such as Kafka or RabbitMQ
• Cloud platform expertise in AWS GCP or Azure including containerization with Docker and Kubernetes
• Knowledge of relational databases (PostgreSQL MySQL) and NoSQL databases (MongoDB Redis)
• Skills in CI/CD pipelines (Jenkins GitHub Actions GitLab CI) and DevOps engineering practices
• Active daily use of AI coding assistants (GitHub Copilot Cursor Claude Code) and frontier LLMs in a fluent not experimental capacity
• Hands-on experience building and deploying at least one MCP server exposing APIs tools or data sources to an LLM agent
• Demonstrated experience designing or implementing an agentic workflow or pipeline that connects multiple tools or services via LLM-orchestrated agents
• Capability to integrate agentic pipelines with enterprise systems via MCP or REST/event APIs
• Familiarity with at least one agent orchestration framework such as LangChain LangGraph CrewAI AutoGen or Spring AI Agents
• Strong critical evaluation of AI-generated code to identify correctness issues security gaps and performance problems
• Genuine learning agility to describe how your engineering practice changed meaningfully in the last 612 months due to new AI tools or model capabilities
• English proficiency at Upper-Intermediate or above (B2)

• Experience building RAG pipelines including chunking embedding and vector stores (pgvector Pinecone Weaviate)
• Prompt engineering skills for development contexts including systematic prompt design evaluation harnesses and iteration workflows
• Familiarity with LLM evaluation frameworks (RAGAS DeepEval) to assess agent output quality
• Experience with function calling and tool-use APIs across multiple frontier models (Anthropic OpenAI Google)
• Exposure to structured agentic SDLC methodologies such as spec-driven development with AI or specification hardening