Principal Core Infrastructure Software Engineer

Oracle Oracle · Enterprise · Nashville, TN +1

Principal Core Infrastructure Software Engineer at Oracle, responsible for defining, building, and operating next-generation AI systems on OCI. This role involves setting architecture and engineering direction for production-grade cloud distributed systems, agentic AI platforms, autonomous workflows, scalable inference infrastructure, and enterprise AI applications. The ideal candidate combines deep distributed systems experience with practical AI-native engineering, including orchestration of LLMs, tools, APIs, memory, retrieval, evaluation, guardrails, and cloud services. The expectation is to ship, scale, and operate reliable, secure, observable, and cost-aware AI platform systems.

What you'd actually do

  1. Lead the development and implementation, and begin to architect, components of scalable distributed systems that support horizontal and vertical scaling to meet system demands, including leveraging distributed state management tools.
  2. Design, architect, and deliver scalable agentic AI systems capable of reasoning, planning, tool use, workflow execution, multi-step task orchestration, and safe human-in-the-loop escalation.
  3. Define scalability, performance, availability, and durability requirements for owned services and components.
  4. Build fault-tolerant systems that support redundancy, replication, automated failover, and in-service upgrades.
  5. Optimize services for high-throughput, low-latency, and large-scale cloud workloads.

Skills

Required

  • Java or Golang or Python
  • Kubernetes
  • Docker
  • cloud-native infrastructure
  • service-to-service communication
  • scalability
  • fault tolerance
  • observability
  • performance analysis
  • SLIs/SLOs
  • incident response practices
  • monitoring
  • tracing
  • experiments
  • reliability programs

Nice to have

  • large scale cloud platforms (e.g., AWS, Azure, Google, Oracle Cloud)
  • LangGraph
  • LangChain
  • CrewAI
  • AutoGen
  • LlamaIndex
  • prompt design
  • structured outputs
  • function/tool calling
  • context management
  • RAG
  • memory
  • tool safety
  • evaluation
  • AI-assisted software development tools
  • enterprise
  • cloud infrastructure
  • regulated
  • security-sensitive
  • mission-critical environments

What the JD emphasized

  • production-grade
  • business-critical environments
  • deeply hands-on
  • production systems
  • high-scale distributed systems
  • production code
  • production readiness criteria

Other signals

  • building and operating next-generation AI systems
  • agentic AI platforms
  • scalable inference infrastructure
  • enterprise AI applications
  • orchestration of LLMs, tools, APIs, memory, retrieval, evaluation, guardrails