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