Java AI

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.