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.