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 practices. In 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. 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 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 team's 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 Requirements 5–10 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 6–12 months due to new AI tools or model capabilities English proficiency at Upper-Intermediate or above (B2+) Nice to have 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