Agentic AI - Java Background

IntroductionAs a member of our Agentic AI team with a Java background, you will be responsible for developing and implementing innovative AI solutions using cutting-edge technologies. You will work closely with our team to create AI-powered applications that drive business growth and enhance user experiences.ResponsibilitiesDevelop and deploy Java microservices using LangChain4j and Spring AIImplement Semantic Kernel for enhanced AI capabilitiesUtilize Retrieval-Augmented Generation (RAG) for advanced AI functionalitiesWork with Milvus, Qdrant, and Pinecone for efficient data storage and retrievalIntegrate LLM (Large Language Model) for natural language processing tasksUtilize Spring Boot and Spring Cloud for building scalable applicationsImplement Hibernate for database managementCreate and maintain RESTful APIs for seamless communication between servicesUtilize PostgreSQL and NoSQL databases for data storage and retrievalWork with Vector Databases, including PGVector and Chroma, for AI model storageImplement messaging systems such as Kafka and RabbitMQ for real-time data processingUtilize WebSockets for bidirectional communication between clients and serversDeploy applications on cloud platforms such as AWS, Azure, and Google Cloud PlatformContainerize applications using Docker and manage container orchestration with KubernetesRequirementsRequired Skills:Proficiency in Java programmingExperience with developing Java microservicesKnowledge of AI technologies such as RAG, LLM, and Semantic KernelExperience with Spring Boot, Spring Cloud, and HibernateUnderstanding of RESTful API design and implementationExperience with PostgreSQL and NoSQL databasesFamiliarity with Vector Databases like PGVector and ChromaExperience with messaging systems such as Kafka and RabbitMQKnowledge of WebSockets for real-time communicationExperience with cloud platforms like AWS, Azure, and Google Cloud PlatformProficiency in Docker and Kubernetes for containerization and orchestrationPreferred SkillsExperience with Milvus, Qdrant, and Pinecone for data storage and retrieval