Java AI Developer (RAG/LLM)

Donato Technologies, Inc

United States

29 days ago

Apply on www.dice.com

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Expert

Experience required

10 years minimum

Working hours

Regular working hours

Job source

www.dice.com

Tech stack

Java (Programming Language) Application Programming Interfaces (APIs) Agile Methodology Artificial Intelligence Amazon Web Services Application Performance Management Microsoft Azure Continuous Integration Document Retrieval Java Platform Enterprise Edition (J2EE) Enterprise Messaging Systems NoSQL

+22 more

OpenShift Standard Sql Search Technologies Systems Integration Google Cloud Enterprise Software Applications Large Language Models Grafana Prompt Engineering Spring-boot Model Validation Indexer Git AI Platforms Kubernetes Apache Kafka Virtual Agents Nim (Programming Language) Restful APIs Docker Jenkins Microservices

Job description

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

Requirements

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

Preferred Skills

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, * 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