Lead Principal AI Infra Engineer, Network Modeling

Oracle; Nashville (TN); On-site; USD 146,000 - 306,000; Full time

Benefits offered by this job

Medical, dental, vision insurance

401(k) with company match

Paid time off

Job summary

Oracle in Nashville is seeking a Lead Principal Software Engineer to shape architecture for AI infrastructure networking. You will own critical distributed services, engineer scalable networks for GPU clusters, and mentor engineers across teams to ensure reliable, safe infrastructure changes.

The role emphasizes hands-on coding, architecture reviews, and driving production-grade solutions that enable large-scale AI workloads with high availability and performance.

Qualifications

• 10+ years of software engineering experience, including distributed systems.
• Strong programming skills and a track record delivering complex production software.
• Deep understanding of concurrency, consistency, fault tolerance, API design, and persistent state.
• Experience making architectural decisions across services and explaining tradeoffs clearly.

Responsibilities

• Lead architecture and hands-on development for distributed services supporting network modeling, configuration, and lifecycle operations across OCI’s AI and cloud infrastructure.
• Evolve network models, APIs, and automation to support expanding GPU infrastructure and new network architectures.
• Design reliable workflows for concurrent and long-running changes, with state management, retries, recovery, and rollback.
• Build safety into network automation through validation, controlled rollout, observability, and limits on failure scope.
• Improve service performance and scalability to accelerate infrastructure provisioning and network changes.

Skills

Distributed systems design

Programming

API design

Concurrency

Fault tolerance

Technical leadership

Job description

Oracle in Nashville is seeking a Lead Principal Software Engineer to shape architecture for AI infrastructure networking. You will own critical distributed services, engineer scalable networks for GPU clusters, and mentor engineers across teams to ensure reliable, safe infrastructure changes.

The role emphasizes hands-on coding, architecture reviews, and driving production-grade solutions that enable large-scale AI workloads with high availability and performance.