Principal AI Agent / Software Engineer- Nashville

Oracle•Nashville, TN

12d•$114,600 - $234,600•Onsite

About The Position

The Principal AI Agent / Software Engineer is a Senior Staff-level, hands-on technical leadership role responsible for defining, building, and operating next-generation AI systems on Oracle Cloud Infrastructure (OCI). The role sets architecture and engineering direction for production-grade agentic AI platforms, autonomous workflows, scalable inference infrastructure, and enterprise AI applications in large-scale, business-critical environments. The engineer translates ambiguous product and platform goals into durable technical strategy, leads multi-team execution without direct authority, and remains hands-on in design, coding, reviews, operations, and incident follow-up. The role combines distributed systems expertise with practical AI-native engineering across LLMs, tools, APIs, memory, retrieval, evaluation, guardrails, and cloud services. It is accountable for delivering reliable, secure, observable, and cost-aware AI platform systems while raising the technical bar across the organization.

Requirements

Bachelor's, Master's, or Ph.D. in Computer Science, AI/ML, Engineering, or a related field, or equivalent practical experience.
6–10+ years of professional software engineering experience, including significant ownership of production systems, or equivalent experience demonstrating Senior Staff / Principal-level impact.
Proven track record as a Staff, Senior Staff, Principal, or equivalent technical leader influencing architecture and execution across multiple teams.
Deep experience designing, building, and operating high-scale distributed systems, cloud services, infrastructure platforms, or AI/ML platform services.
Hands-on experience with production AI systems, agentic AI applications, autonomous workflows, tool-using agents, multi-step orchestration, or multi-agent systems.
Practical experience with orchestration frameworks such as LangGraph, LangChain, CrewAI, AutoGen, LlamaIndex, or similar ecosystems.
Deep understanding of LLM application patterns, including prompt design, structured outputs, function/tool calling, context management, RAG, memory, tool safety, and evaluation.
Strong programming skills in Python and ability to contribute high-quality production code, reviews, tests, and debugging in complex distributed environments.
Strong expertise with Kubernetes, Docker, cloud-native infrastructure, service-to-service communication, scalability, fault tolerance, observability, and performance analysis.
Experience defining SLIs/SLOs, production readiness criteria, incident response practices, monitoring, tracing, experiments, and reliability programs for AI or distributed systems.
Strong understanding of AI safety, governance, security, and operational risks for autonomous or semi-autonomous systems, including data handling, access control, auditability, and human accountability.
Excellent written and verbal communication, with demonstrated ability to lead technical direction, resolve ambiguity, and influence senior stakeholders.

Nice To Haves

Experience optimizing large-scale GPU inference or training workloads for latency, throughput, utilization, availability, and cost.
Experience building or operating model serving, inference gateways, agent runtimes, workflow engines, developer platforms, or internal AI productivity platforms.
Experience integrating AI systems with enterprise APIs, databases, cloud services, vector databases, embeddings, retrieval systems, identity systems, and policy enforcement layers.
Experience with LLM fine-tuning, long-context systems, reasoning models, model routing, caching, batching, quantization, or emerging generative AI research.
Experience building evaluation frameworks for agentic systems, including offline evaluations, online experiments, golden tasks, adversarial testing, regression gates, and observability dashboards.
Experience using AI-assisted software development tools such as Codex, Claude Code, Cursor, Copilot, or similar systems in large-scale engineering environments.
Track record of defining architectural standards, platform capabilities, or engineering practices adopted across multiple teams or organizations.
Experience in enterprise, cloud infrastructure, regulated, security-sensitive, or mission-critical environments.

Responsibilities

Serve as a senior technical owner for OCI AI platform capabilities, including agent execution, inference systems, model serving, AI workflow orchestration, evaluation, and observability.
Design, architect, and deliver scalable agentic AI systems for reasoning, planning, tool use, workflow execution, multi-step task orchestration, and safe human-in-the-loop escalation.
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.
Lead architecture across distributed services optimized for low latency, high throughput, GPU efficiency, reliability, cost, operability, and secure multi-tenant operation.
Define service boundaries, APIs, data models, state management, consistency tradeoffs, failure modes, SLIs/SLOs, rollout strategies, and operational readiness criteria for AI platform services.
Drive technical strategy across infrastructure, platform, security, data, and application engineering teams, translating broad goals into executable multi-quarter plans and measurable milestones.
Integrate AI agents securely and reliably with enterprise APIs, cloud services, databases, identity systems, secrets management, and external systems.
Establish AgentOps and LLMOps practices for tracing, monitoring, evaluation suites, regression testing, experimentation, safety guardrails, prompt and tool versioning, and production reliability.
Evaluate and operationalize emerging technologies in generative AI, agentic workflows, inference optimization, long-context systems, reasoning models, AI developer tooling, and agentic-first development.
Drive engineering excellence through code and design reviews, test strategy, deployment automation, incident analysis, documentation, and AI-assisted development practices.
Mentor Staff and senior engineers, raise architectural standards, and influence engineering practices across OCI without requiring direct management authority.
Own critical production outcomes, including reliability, performance, security posture, cost efficiency, and supportability for delivered systems.

Benefits

Medical, dental, and vision insurance, including expert medical opinion.
Short-term and long-term disability insurance.
Life insurance and AD&D.
Supplemental life insurance for employees, spouses, and children.
Health care and dependent care Flexible Spending Accounts.
Pre-tax commuter and parking benefits.
401(k) Savings and Investment Plan with company match.
Flexible Vacation for eligible salaried employees or accrued vacation for other eligible employees.
11 paid holidays.
Paid sick leave: 72 hours upon hire, refreshed each calendar year, with unused balance carrying over up to a 112-hour cap.
Paid parental leave.
Adoption assistance.
Employee Stock Purchase Plan.
Financial planning and group legal benefits.
Voluntary benefits, including auto, homeowner, and pet insurance.
Bonus, equity, and compensation deferral may be available.