We are looking for a Senior Java Engineer – AI Native to join our team and drive the design and delivery of scalable Java applications while embedding AI-native practices across the full software development lifecycle. This role combines deep Java engineering expertise with hands-on experience building agentic pipelines and MCP integrations that connect enterprise systems to LLM-based agents.
The position requires 3 days of work from the office.
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
- Architect and implement 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 and ServiceNow via MCP connectors or REST/event-driven APIs
- Use AI coding assistants and frontier LLMs across the full development lifecycle daily and critically evaluate AI outputs for correctness, security and edge cases before code commits
- 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 team's 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
Requirements
- 5–10 years of hands-on Java development experience in production environments
- Proficiency in Spring Boot, Spring MVC and Spring Security with RESTful API design
- Experience with microservices and event-driven patterns using Kafka or RabbitMQ
- Background in cloud platforms such as AWS, GCP or Azure including Docker and Kubernetes containerization
- Knowledge of relational databases such as PostgreSQL and MySQL alongside NoSQL databases such as MongoDB and Redis
- Familiarity with CI/CD pipelines including Jenkins, GitHub Actions and GitLab CI and DevOps engineering practices
- Active daily use of AI coding assistants such as GitHub Copilot, Cursor or Claude Code and frontier LLMs, applied fluently rather than experimentally
- Hands-on experience building and deploying at least one MCP server that exposes APIs, tools or data sources to an LLM agent
- Showcase of 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 through direct hands-on build experience
- Working knowledge of at least one agent orchestration framework such as LangChain, LangGraph or CrewAI
- Understanding of how to critically evaluate AI-generated code, identifying correctness issues, security gaps and performance problems, along with genuine learning agility reflected in evolving engineering practice over the last 6–12 months due to new AI tools or model capabilities
- Upper-Intermediate English proficiency or above (B2+)
Nice to have
- Experience building RAG (Retrieval-Augmented Generation) pipelines covering chunking, embedding and vector stores such as pgvector, Pinecone or Weaviate
- Skills in prompt engineering for development contexts including systematic prompt design, evaluation harnesses and iteration workflows
- Familiarity with LLM evaluation frameworks such as RAGAS or DeepEval to assess agent output quality
- Experience with function calling and tool-use APIs across multiple frontier models from Anthropic, OpenAI and Google
- Exposure to structured agentic SDLC methodologies such as spec-driven development with AI or specification hardening
