Java AI Developer (RAG/LLM)
Donato Technologies, Inc
United States
3 months ago
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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
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
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