JAVA With AI 4+

Java Developer (5+ Years) – Job Description

Role: Senior Java Developer (5–7 Years) Responsibilities

• Design, develop, and maintain enterprise Java applications.
• Build RESTful APIs using Java and Spring Boot.
• Develop microservices-based applications.
• Optimize application performance and scalability.
• Work with relational and NoSQL databases.
• Collaborate with frontend, DevOps, QA, and product teams.
• Write unit and integration tests.
• Participate in code reviews and mentor junior developers.
• Integrate AI/LLM services into enterprise applications where required. Required Skills
• 5–7 years of Java development experience.
• Strong knowledge of Java 8/11/17.
• Spring Boot, Spring MVC, Spring Security.
• Microservices architecture.
• REST APIs.
• Hibernate/JPA.
• SQL (Oracle, PostgreSQL, MySQL).
• Kafka or RabbitMQ.
• Docker and Kubernetes.
• Git, Maven, Gradle.
• CI/CD (Jenkins, GitHub Actions, Azure DevOps). AI Skills (Preferred)
• Experience integrating LLM APIs (OpenAI, Azure OpenAI, Gemini).
• RAG-based application development.
• Prompt engineering.
• Vector databases (Pinecone, Milvus, pgvector).
• LangChain4j or Spring AI.
• AI agents and workflow automation. Senior Java AI Developer (8–10+ Years) – Job Description

Role: Lead/Senior Java AI Developer (8–10+ Years) Responsibilities

• Architect enterprise-grade Java applications.
• Lead microservices design and cloud-native development.
• Build scalable AI-powered applications.
• Integrate Generative AI and LLM solutions into enterprise platforms.
• Design RAG pipelines.
• Build AI assistants, chatbots, and intelligent automation.
• Lead technical design discussions and mentor engineering teams.
• Optimize AI application performance and cost.
• Ensure security, governance, and observability of AI services. Required Skills
• 8–10+ years in Java development.
• Expert in Java 17+.
• Spring Boot, Spring Cloud.
• Microservices architecture.
• Distributed systems.
• Event-driven architecture.
• Kafka.
• Docker, Kubernetes.
• AWS, Azure, or GCP.
• SQL and NoSQL databases.
• Redis, Elasticsearch.
• CI/CD pipelines.
• System design and architecture. AI & GenAI Skills (Must Have)
• Experience with OpenAI, Azure OpenAI, Gemini, or Anthropic APIs.
• Spring AI or LangChain4j.
• RAG architecture.
• MCP (Model Context Protocol).
• AI agents and multi-agent systems.
• Prompt engineering.
• Vector databases (Pinecone, Weaviate, Milvus, pgvector).
• Embedding models and semantic search.
• AI observability and evaluation.
• Experience deploying AI applications in production. Nice to Have
• Python for AI workflows.
• TensorFlow or PyTorch basics.
• Knowledge graphs.
• LlamaIndex.
• Hugging Face ecosystem.
• Fine-tuning and model evaluation.
• Experience with enterprise AI governance and security.