Java AI
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Summary
Remote US role (10–20 years experience) building Java and Spring Boot services that integrate AI into enterprise applications: REST APIs for AI assistants, RAG workflows, document ingestion pipelines, vector search, prompt design, secure tool calling, output evaluation, and CI/CD plus production support.
Role & responsibilities
- Gather business requirements and identify practical opportunities to integrate AI into enterprise applications.
- Design and develop Java and Spring Boot services that connect business systems with AI models.
- Build REST APIs supporting AI assistants, document analysis, summarization, and intelligent search.
- Implement RAG workflows to generate responses grounded in approved enterprise documents.
- Develop document ingestion pipelines covering extraction, chunking, embeddings, indexing, and retrieval.
- Integrate vector databases and configure search filters to improve retrieval relevance.
- Design prompts, structured outputs, and validation rules for reliable application behavior.
- Implement controlled tool calling so AI applications can interact with authorized business services.
- Apply authentication, authorization, sensitive-data protection, and safeguards against prompt injection.
- Evaluate AI outputs for accuracy, relevance, unsupported statements, and adherence to business requirements.
- Optimize application performance, model usage, response time, and operating costs.
- Develop automated tests and integrate application builds and deployments into CI/CD pipelines.
- Monitor production services, investigate failures, and resolve application and integration issues.
- Collaborate with architects, data scientists, security teams, and business stakeholders.
- Document solution architecture, provide technical recommendations, and support knowledge transfer to client teams.
Preferred candidate profile
- Gather business requirements and identify practical opportunities to integrate AI into enterprise applications.
- Design and develop Java and Spring Boot services that connect business systems with AI models.
- Build REST APIs supporting AI assistants, document analysis, summarization, and intelligent search.
- Implement RAG workflows to generate responses grounded in approved enterprise documents.
- Develop document ingestion pipelines covering extraction, chunking, embeddings, indexing, and retrieval.
- Integrate vector databases and configure search filters to improve retrieval relevance.
- Design prompts, structured outputs, and validation rules for reliable application behavior.
- Implement controlled tool calling so AI applications can interact with authorized business services.
- Apply authentication, authorization, sensitive-data protection, and safeguards against prompt injection.
- Evaluate AI outputs for accuracy, relevance, unsupported statements, and adherence to business requirements.
- Optimize application performance, model usage, response time, and operating costs.
- Develop automated tests and integrate application builds and deployments into CI/CD pipelines.
- Monitor production services, investigate failures, and resolve application and integration issues.
- Collaborate with architects, data scientists, security teams, and business stakeholders.
- Document solution architecture, provide technical recommendations, and support knowledge transfer to client teams.