Oracle Health is seeking a Principal AI Agent Architect to define and build the technical foundation for a new AI engineering capability focused on production AI agents, LLM-enabled workflow automation, semantic intelligence, and governed AI integration across analytics and data platforms.
This role will own the end-to-end architecture for agent-based systems that interact with enterprise tools and data in safe, scalable, and observable ways. The architect will define patterns for agent orchestration, tool and function calling, retrieval and grounding, evaluation, model selection, security, auditability, and runtime controls. This person will also establish reusable platform standards that enable multiple AI use cases to be delivered efficiently and consistently.
In addition to deep technical architecture responsibilities, this role is expected to serve as a founding technical leader for the team. This individual will act as the senior technical lead for the AI team, partnering directly with leadership to shape priorities, hiring, team structure, and roadmap sequencing. The ideal candidate brings both strong hands-on architectural depth and the maturity to help build and guide a multidisciplinary AI team in an environment where domain experts provide business and data context, while this team provides the AI expertise.
This role is critical to ensuring Oracle Health’s AI capabilities are built on strong technical foundations rather than one-off prototypes, and that the team can be self-sufficient in AI engineering, LLM systems, and agent-based development from the start.
Internal Responsibilities
- Define the reference architecture for AI agents, LLM orchestration, tool calling, retrieval, grounding, memory, and evaluation.
- Design secure integration patterns between AI systems and enterprise platforms including databases, metadata systems, analytics tools, telemetry systems, and developer workflows.
- Establish standards for safe tool execution, least-privilege access, authentication, secrets handling, audit logging, and production controls.
- Build reusable platform capabilities and technical patterns that accelerate the delivery of multiple AI agents and workflow automations.
- Guide model selection, prompt and tool architecture, latency and cost tradeoffs, fallback patterns, and reliability design.
- Partner directly with leadership on priorities, tradeoffs, roadmap sequencing, and hiring plans for the new AI team.
- Help recruit, interview, and assess candidates across multiple disciplines including applied AI engineering, semantic engineering, and platform reliability.
- Provide technical leadership and mentorship across architecture, coding patterns, delivery standards, and production quality.
- Operate effectively in an ambiguous, early-stage team environment and help shape the team’s technical operating model.
- Serve as a senior technical voice with both AI specialists and non-AI business and engineering leaders.
- Define release standards, technical review criteria, and evaluation frameworks for AI-enabled features.
External Responsibilities
- Define the reference architecture for AI agents, LLM orchestration, tool calling, retrieval, grounding, memory, and evaluation.
- Design secure integration patterns between AI systems and enterprise platforms including databases, metadata systems, analytics tools, telemetry systems, and developer workflows.
- Establish standards for safe tool execution, least-privilege access, authentication, secrets handling, audit logging, and production controls.
- Build reusable platform capabilities and technical patterns that accelerate the delivery of multiple AI agents and workflow automations.
- Guide model selection, prompt and tool architecture, latency and cost tradeoffs, fallback patterns, and reliability design.
- Partner directly with leadership on priorities, tradeoffs, roadmap sequencing, and hiring plans for the new AI team.
- Help recruit, interview, and assess candidates across multiple disciplines including applied AI engineering, semantic engineering, and platform reliability.
- Provide technical leadership and mentorship across architecture, coding patterns, delivery standards, and production quality.
- Operate effectively in an ambiguous, early-stage team environment and help shape the team’s technical operating model.
- Serve as a senior technical voice with both AI specialists and non-AI business and engineering leaders.
- Define release standards, technical review criteria, and evaluation frameworks for AI-enabled features.
