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Senior Java Engineer - AI Native

Office in India: Coimbatore, & 4 others
Java& 17 others
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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