Senior Manager, Application Software Engineering

Oracle Oracle · Enterprise · Nashville, TN +1

Senior Manager to lead an AI Platform and Productivity Engineering team at Oracle Cloud Infrastructure (OCI). The role focuses on delivering secure, reliable, production-grade AI capabilities to improve employee productivity and accelerate enterprise AI adoption. Responsibilities include managing the team, translating strategy into roadmaps, delivering enterprise plugins, Codex enablement tooling, shared architectures, and dynamic model enforcement. The role also involves establishing engineering practices, driving observability, resilience, and managing technical dependencies. Key focus areas include reusable platform capabilities, AI governance, security, privacy, cost optimization, and LLM application patterns like RAG, tool calling, and agentic workflows.

What you'd actually do

  1. Lead, develop, and scale the AI Platform and Productivity Engineering team, accountable for delivery of secure, reliable, production-grade AI capabilities across enterprise productivity workflows.
  2. Translate enterprise AI strategy into an executable engineering roadmap, aligning team priorities with OCI objectives, platform standards, security requirements, cost efficiency, and measurable adoption outcomes.
  3. Deliver enterprise-grade plugins, Codex enablement tooling, shared plugin and service architecture, and dynamic model-enforcement capabilities that enable teams to adopt AI safely and consistently.
  4. Partner with the Enterprise AI strategy team to implement the common operating model, including reusable patterns, quality guardrails, usage telemetry, cost intelligence, and adoption guidance.
  5. Establish and operate a plugin reuse registry and certification process, enabling teams to discover trusted capabilities, avoid duplicate development, and retire redundant or low-value AI assets.

Skills

Required

  • software development
  • programming and/or scripting languages (e.g., SQL, C/C++, JavaScript)
  • databases
  • people leadership/management
  • working with operating budgets and/or project financials
  • cloud platforms (e.g., AWS, Azure, Google, Oracle Cloud)

Nice to have

  • building, integrating, or operating AI-enabled applications, developer tools, productivity platforms, or generative AI capabilities
  • large language model (LLM) application patterns
  • prompt design
  • retrieval-augmented generation
  • tool/function calling
  • agentic workflows
  • evaluation methods
  • AI governance
  • security
  • privacy
  • model-access controls
  • policy-driven model selection
  • optimizing AI cost and performance
  • model routing
  • token and context management
  • usage telemetry
  • caching
  • workload optimization
  • designing reusable AI platform capabilities, plugins, services, or integrations

What the JD emphasized

  • secure
  • reliable
  • production-grade
  • enterprise AI adoption
  • Codex enablement tooling
  • shared plugin and service architecture
  • dynamic model-enforcement implementation
  • reusable platform capabilities
  • AI governance
  • security
  • privacy
  • model-access controls
  • policy-driven model selection
  • cost and performance optimization
  • LLM application patterns
  • prompt design
  • retrieval-augmented generation
  • tool/function calling
  • agentic workflows
  • evaluation methods

Other signals

  • AI Platform
  • enterprise AI adoption
  • productivity workflows
  • Codex enablement tooling
  • shared plugin and service architecture
  • dynamic model-enforcement implementation
  • reusable platform capabilities
  • AI governance
  • security
  • privacy
  • model-access controls
  • policy-driven model selection
  • cost and performance optimization
  • LLM application patterns
  • prompt design
  • retrieval-augmented generation
  • tool/function calling
  • agentic workflows
  • evaluation methods