## 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 - 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

.