Sr Full stack Java Developer

AI Full Stack Java Developer

- Designed and developed scalable AI-powered full-stack applications using Java, Spring Boot, React/Angular, REST APIs, and cloud-native technologies.

- Integrated Generative AI and Large Language Models (LLMs) into enterprise applications to deliver intelligent search, content generation, recommendation, summarization, and conversational capabilities.

- Built AI-enabled backend services using Java, Spring Boot, Spring AI, LangChain/LangGraph concepts, and RESTful APIs , ensuring secure and maintainable application architecture.

- Developed Retrieval-Augmented Generation (RAG) solutions by integrating LLMs with enterprise documents, knowledge bases, vector databases, and semantic search.

- Implemented prompt engineering, prompt templates, response validation, context management, and AI guardrails to improve accuracy, consistency, and reliability of AI-generated responses.

- Developed responsive and reusable frontend components using React/Angular, TypeScript, JavaScript, HTML5, and CSS3 , integrating them with AI-enabled backend services.

- Designed microservices using Spring Boot, Spring Cloud, API Gateway, and service-to-service communication for highly scalable distributed applications.

- Developed and consumed REST and event-driven APIs , integrating third-party AI platforms, enterprise systems, databases, and external services.

- Worked with OpenAI/Azure OpenAI or equivalent LLM platforms , embedding models, vector search, and AI APIs into production applications.

- Implemented vector-based knowledge retrieval using technologies such as Pinecone, Azure AI Search, Elasticsearch, or PostgreSQL with pgvector .

- Designed data persistence solutions using PostgreSQL, MySQL, MongoDB, and Redis , selecting appropriate storage mechanisms based on application requirements.

- Applied Spring Security, OAuth 2.0, JWT, RBAC, and API security practices to protect enterprise and AI-powered applications.

- Implemented asynchronous and event-driven processing using Kafka, RabbitMQ, or cloud messaging services for high-volume workloads.

- Containerized applications using Docker and deployed microservices to Kubernetes and cloud platforms such as AWS, Azure, or GCP .

- Developed CI/CD pipelines using Jenkins, Maven, Git, GitHub/GitLab, and automated deployment workflows .

- Implemented automated unit, integration, API, and end-to-end testing using JUnit, Mockito, REST Assured, Selenium, Playwright, or Cypress .

- Added observability through logging, metrics, distributed tracing, health checks, and application monitoring , helping identify performance and AI-service issues.

- Optimized application performance through caching, database tuning, API optimization, asynchronous processing, and efficient LLM/API utilization .

- Collaborated with product managers, architects, data scientists, QA engineers, and DevOps teams to transform business requirements into production-ready AI solutions.

Requirements

Requirements; 5+ years of professional software development experience with strong expertise in Java and Spring Boot .; Strong hands-on experience building full-stack applications using Java, Spring Boot, REST APIs, React or Angular, JavaScript, and TypeScript.; Experience designing and developing microservices-based, scalable, and cloud-native applications .; Practical experience integrating Generative AI, Large Language Models (LLMs), and AI APIs into enterprise applications.; Strong understanding of RAG architecture, embeddings, vector databases, semantic search, prompt engineering, and LLM orchestration .; Experience working with OpenAI, Azure OpenAI, AWS Bedrock, Google Vertex AI, or similar AI platforms .; Knowledge of Spring AI, LangChain/LangGraph, or comparable AI application frameworks is highly desirable.; Experience developing and consuming RESTful APIs, JSON-based services, and third-party integrations .; Strong database experience with PostgreSQL, MySQL, MongoDB, Redis, or similar technologies .; Experience with Kafka, RabbitMQ, or other event-driven messaging platforms .; Hands-on experience with Docker, Kubernetes, CI/CD, Jenkins, Maven, Git, and cloud deployment .