What are the responsibilities and job description for the Senior Java AI Developer (RAG/LLM) position at Geethanjali College of Engineering and Technology?
The client is looking for a Senior Java AI Developer to develop enterprise-scale Generative AI applications. The ideal candidate will possess strong Java backend expertise along with hands-on experience building Retrieval-Augmented Generation (RAG) solutions and integrating enterprise applications with Large Language Models.
Required Skills
Required Skills
- 10 years of Java development experience
- Strong experience with Java 17 and Spring Boot
- Hands-on experience building Retrieval-Augmented Generation (RAG) systems
- Experience with Spring AI, LangChain4j, or similar Java AI frameworks
- Experience integrating OpenAI, Azure OpenAI, Claude, Gemini, or Llama models
- Strong knowledge of vector databases such as Pinecone, Milvus, Weaviate, FAISS, or ChromaDB
- Experience implementing semantic search and document retrieval pipelines
- Strong REST API and Microservices development experience
- Experience with Kafka or other messaging platforms
- Experience with Docker, Kubernetes, and OpenShift
- Experience with AWS, Azure, or Google Cloud Platform
- Strong SQL and NoSQL database experience
- Experience with Git, Jenkins, and CI/CD
- Experience with Agile development methodologies
- Banking or financial services experience
- Knowledge of AI agents and autonomous workflows
- Experience with Spring AI ecosystem
- Experience implementing enterprise security standards
- Experience with AI governance and monitoring
- Knowledge of prompt engineering and model evaluation
- Design and develop enterprise Java-based AI applications
- Build scalable RAG architectures using Java and Spring Boot
- Integrate LLMs into enterprise applications
- Develop document ingestion, indexing, and retrieval pipelines
- Build secure APIs for AI services
- Optimize retrieval accuracy and response quality
- Collaborate with architects, ML engineers, and business teams
- Deploy AI services using containerized cloud platforms
- Ensure application performance, scalability, and compliance
- Provide technical leadership and mentor development teams
- Experience with LangGraph or AI Agent frameworks
- MCP (Model Context Protocol) implementation experience
- Knowledge Graph integration
- AI observability tools (LangSmith, Arize AI, TruLens)
- NVIDIA NIM or enterprise inference platforms
- Financial regulatory compliance experience
- Exposure to Agentic AI architectures