Senior AI Agent Engineer

Oracle Corporation; United States; On-site; USD 140,000 - 210,000; Full time

Job summary

Oracle Health is seeking a Senior AI Agent Engineer to build production-grade AI agents and workflow automation that accelerate analytics delivery and reduce manual engineering work across our analytics and reporting ecosystem.

This engineer will design AI-driven workflows that integrate with enterprise systems and support use cases such as SQL generation, pipeline scaffolding, data exploration, metric design support, insight discovery, executive narrative creation, and report/dashboard

Responsibilities

• Design, build, test, and deploy production AI agents and AI-assisted workflows for analytics and reporting use cases.
• Implement agents that support tasks such as SQL generation, pipeline creation, metric design support, insight discovery, executive narrative generation, and dashboard/report scaffolding.
• Integrate AI capabilities with enterprise tools, data platforms, semantic services, and existing engineering workflows.
• Develop structured tool-calling patterns, validation logic, retry handling, and grounded output generation.
• Build evaluation and testing approaches for AI workflows, including regression testing, quality validation, and failure analysis.
• Partner with architects, semantic engineers, and domain teams to ensure AI outputs are useful, accurate, and operationally practical.
• Optimize workflows for quality, speed, cost, and maintainability.
• Contribute to AI engineering standards, reusable components, and continuous improvement across the team.

Job description

Oracle Health is seeking a Senior AI Agent Engineer to build production AI agents and workflow automation capabilities that accelerate analytics delivery, improve insight generation, and reduce manual engineering work across our analytics and reporting ecosystem.

This engineer will design and implement AI-driven workflows that integrate with enterprise systems and support use cases such as SQL generation, pipeline scaffolding, data exploration, metric design support, insight discovery, executive narrative creation, and report/dashboard acceleration. The role requires strong software engineering fundamentals combined with hands-on experience building LLM-enabled applications that are grounded, testable, and production-ready.

The ideal candidate is a builder who can move from concept to implementation, balancing speed with safety, evaluation, and maintainability. This person should be able to work across prompts, tools, code, APIs, structured outputs, validation, and workflow integration to deliver real productivity gains.

Internal Responsibilities

• Design, build, test, and deploy production AI agents and AI-assisted workflows for analytics and reporting use cases.
• Implement agents that support tasks such as SQL generation, pipeline creation, metric design support, insight discovery, executive narrative generation, and dashboard/report scaffolding.
• Integrate AI capabilities with enterprise tools, data platforms, semantic services, and existing engineering workflows.
• Develop structured tool-calling patterns, validation logic, retry handling, and grounded output generation.
• Build evaluation and testing approaches for AI workflows, including regression testing, quality validation, and failure analysis.
• Partner with architects, semantic engineers, and domain teams to ensure AI outputs are useful, accurate, and operationally practical.
• Optimize workflows for quality, speed, cost, and maintainability.
• Contribute to AI engineering standards, reusable components, and continuous improvement across the team.

External Responsibilities

• Design, build, test, and deploy production AI agents and AI-assisted workflows for analytics and reporting use cases.
• Implement agents that support tasks such as SQL generation, pipeline creation, metric design support, insight discovery, executive narrative generation, and dashboard/report scaffolding.
• Integrate AI capabilities with enterprise tools, data platforms, semantic services, and existing engineering workflows.
• Develop structured tool-calling patterns, validation logic, retry handling, and grounded output generation.
• Build evaluation and testing approaches for AI workflows, including regression testing, quality validation, and failure analysis.
• Partner with architects, semantic engineers, and domain teams to ensure AI outputs are useful, accurate, and operationally practical.
• Optimize workflows for quality, speed, cost, and maintainability.
• Contribute to AI engineering standards, reusable components, and continuous improvement across the team.