Java Full Stack Developer
Role details
Job location
Tech stack
Job description
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Design and develop scalable RESTful microservices and APIs using Java 17/21 and Spring Boot, following cloud-native and domain-driven design principles.
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Architect and implement LLM-powered features using LangChain4j or Spring AI - including tool/function calling, agentic chains, RAG pipelines, and prompt engineering.
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Integrate LLM providers (OpenAI, Azure OpenAI, AWS Bedrock, or Anthropic Claude) into production systems with proper guardrails, hallucination mitigation, and evaluation frameworks.
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Build fault-tolerant service integrations using Feign clients and Resilience4j (circuit breakers, retries, rate limiters) to ensure system reliability against upstream failures.
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Implement asynchronous workflows using Kafka or similar messaging platforms; design job-ID-based async response patterns for long-running operations.
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Develop and maintain React-based front-end components, consuming REST APIs and handling loading, error, and success states cleanly.
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Manage conversation state and session context in MongoDB or equivalent NoSQL stores to enable multi-turn AI interactions and follow-up reasoning.
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Collaborate with product, QA, and platform teams in Agile sprints; contribute to design reviews, code reviews, and technical documentation.
Requirements
We are seeking a Java Full Stack Developer with hands-on backend engineering depth and demonstrated experience integrating Large Language Model (LLM) capabilities into enterprise-grade applications. You will architect and build scalable microservices using Spring Boot, design agentic AI workflows using LangChain4j or Spring AI, and contribute across the full stack from React-based frontends to cloud-deployed backends. You will own end-to-end feature delivery for AI-powered modules, implement resilient LLM integration patterns, and drive technical best practices across the team., 8+ years of professional software development experience with Java; strong command of Java 17/21 features including virtual threads and modern concurrency patterns. Proven expertise with Spring Boot, Spring Security, Spring Data JPA, and microservices architecture; experience with API Gateway patterns and JWT-based authentication. Hands-on experience integrating LLMs via APIs (OpenAI, Azure OpenAI, AWS Bedrock, or Anthropic); practical knowledge of prompt engineering, RAG, embeddings, and vector databases. Experience with LangChain4j, Spring AI, or equivalent Java-native LLM frameworks for building agentic, tool-calling, or multi-step AI orchestration workflows. Proficiency in front-end development using React.js; ability to build, debug, and optimize component-based UIs and REST API integrations. Solid understanding of asynchronous messaging (Kafka, RabbitMQ), distributed systems patterns, and cloud deployment (AWS, Azure, or Google Cloud Platform) Ability to deploy and fine tune AI LLM models on devices like computers , routers etc.….Experience in Model API's