Principal Ai/llm Agent Architect

Oracle Oracle · Enterprise · United States

Seeking a Principal AI Agent Architect to define and build the technical foundation for production AI agents, LLM-enabled workflow automation, and governed AI integration. This role owns the end-to-end architecture for agent-based systems interacting with enterprise tools and data, defining patterns for orchestration, tool use, grounding, evaluation, and security. The architect will also establish reusable platform standards and serve as a founding technical leader for the AI team.

What you'd actually do

  1. Define the reference architecture for AI agents, LLM orchestration, tool calling, retrieval, grounding, memory, and evaluation.
  2. Design secure integration patterns between AI systems and enterprise platforms including databases, metadata systems, analytics tools, telemetry systems, and developer workflows.
  3. Establish standards for safe tool execution, least-privilege access, authentication, secrets handling, audit logging, and production controls.
  4. Build reusable platform capabilities and technical patterns that accelerate the delivery of multiple AI agents and workflow automations.
  5. Guide model selection, prompt and tool architecture, latency and cost tradeoffs, fallback patterns, and reliability design.

Skills

Required

  • AI agent architecture
  • LLM orchestration
  • tool and function calling
  • retrieval-augmented generation (RAG)
  • model evaluation
  • enterprise system integration
  • security and auditability
  • platform development
  • technical leadership

Nice to have

  • semantic intelligence
  • governed AI integration
  • prompt engineering
  • model selection
  • latency and cost optimization

What the JD emphasized

  • founding technical leader
  • senior technical lead
  • build and guide a multidisciplinary AI team
  • critical to ensuring Oracle Health’s AI capabilities are built on strong technical foundations rather than one-off prototypes
  • self-sufficient in AI engineering, LLM systems, and agent-based development from the start
  • shape priorities, hiring, team structure, and roadmap sequencing
  • help recruit, interview, and assess candidates

Other signals

  • production AI agents
  • LLM-enabled workflow automation
  • agent orchestration
  • tool and function calling
  • governed AI integration