Senior AI Software Engineer

Oracle Oracle · Enterprise · Nashville, TN +1

Senior AI Software Engineer role focused on building and owning scalable agentic AI systems and their underlying inference and serving infrastructure within Oracle's OCI AI Innovation organization. Responsibilities include designing, architecting, and delivering production-grade services for agent execution, tool use, memory, context management, and integrating with enterprise systems, while optimizing for performance, reliability, and cost. The role also emphasizes establishing robust AgentOps and LLMOps practices for monitoring, evaluation, and reliability.

What you'd actually do

  1. 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.
  2. Serve as a technical owner for OCI AI platform capabilities, including agent execution, inference systems, model serving, AI workflow orchestration, evaluation, and observability.
  3. Build production-grade services for tool calling, agent memory, context management, Model Context Protocol (MCP) integration, vector retrieval, multi-agent coordination, policy enforcement, and evaluation.
  4. Develop distributed services optimized for low latency, high throughput, GPU efficiency, reliability, cost, operability, and secure multi-tenant operation.
  5. Establish AgentOps and LLMOps practices for tracing, monitoring, eval suites, regression testing, experimentation, safety guardrails, prompt/tool versioning, and production reliability.

Skills

Required

  • Software Engineering
  • Technical Leadership
  • System Design
  • Production Delivery
  • Agentic AI Systems
  • Reasoning and Planning
  • Tool Use
  • Workflow Execution
  • Multi-step Task Orchestration
  • Human-in-the-loop Escalation
  • Inference Systems
  • Model Serving
  • AI Workflow Orchestration
  • Evaluation
  • Observability
  • Tool Calling
  • Agent Memory
  • Context Management
  • Vector Retrieval
  • Multi-agent Coordination
  • Policy Enforcement
  • Distributed Services
  • Low Latency
  • High Throughput
  • GPU Efficiency
  • Reliability
  • Cost Optimization
  • Operability
  • Secure Multi-tenant Operation
  • Service Boundaries
  • APIs
  • Data Models
  • State Management
  • Consistency Tradeoffs
  • Failure Modes
  • SLIs/SLOs
  • Rollout Strategies
  • Operational Readiness
  • Enterprise API Integration
  • Cloud Service Integration
  • Database Integration
  • Identity System Integration
  • Secrets Management
  • AgentOps
  • LLMOps
  • Tracing
  • Monitoring
  • Eval Suites
  • Regression Testing
  • Experimentation
  • Safety Guardrails
  • Prompt Versioning
  • Tool Versioning
  • Production Reliability
  • Generative AI
  • Inference Optimization
  • Long-context Systems
  • Reasoning Models
  • AI Developer Tooling
  • Agentic-first Development
  • Code Reviews
  • Design Reviews
  • Test Strategy
  • Deployment Automation
  • Incident Analysis
  • Documentation
  • AI-assisted Development Practices

Nice to have

  • Codex
  • Claude Code
  • Cursor
  • Copilot

What the JD emphasized

  • scalable agentic AI systems
  • production-grade services
  • low latency, high throughput, GPU efficiency
  • AgentOps and LLMOps practices
  • reliability, performance, security posture, cost efficiency, and supportability

Other signals

  • design, architect, and deliver scalable agentic AI systems
  • Serve as a technical owner for OCI AI platform capabilities
  • Build production-grade services for tool calling, agent memory, context management
  • Develop distributed services optimized for low latency, high throughput, GPU efficiency
  • Establish AgentOps and LLMOps practices