Senior Java Engineer - AI Native

Job description

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