Lead Principal Platform Software Engineer

Join the OCI AI team to build and operate cloud platforms and services that power AI workloads at scale. In this IC5 individual contributor role, you will own complex technical initiatives from architecture and implementation through production operations, translating ambiguous customer and business needs into secure, reliable, and efficient solutions.

You will combine deep distributed systems expertise with hands-on software development, guide technical decisions across teams, and mentor engineers. Adhere to and improves execution of all phases of the software development lifecycle; coach engineers across teams or units to drive improvements.

Act as subject matter expert within the organization to develop and guide new software features and enhancements following design specifications, ensuring accessibility, and develop documents to clarify software. Your work will shape shared platform capabilities, improve service performance and operational excellence, and deliver measurable customer impact.

We take an AI-forward approach to engineering. You will use AI throughout the software development lifecycle and help teams adopt effective AI-assisted workflows for design, coding, testing, review, debugging, and operations. You will pair these capabilities with rigorous validation, sound engineering judgment, and accountability for production outcomes.

• Own the technical direction and end-to-end delivery of complex initiatives spanning multiple teams, from requirements and architecture through implementation, rollout, and operations.
• Design and build distributed platforms and services for OCI AI workloads, balancing availability, latency, throughput, scalability, resource utilization, and cost.
• Write and review critical production code, develop prototypes to resolve technical uncertainty, and lead resolution of complex engineering problems.
• Establish shared platform capabilities and standards for identity, configuration, APIs, client libraries, interoperability, and lifecycle management.
• Lead AI-driven engineering practices across design, implementation, testing, code review, debugging, documentation, and operational troubleshooting.
• Build reusable AI-assisted tools and workflows; measure their impact on delivery speed, software quality, developer effort, and operational effectiveness.
• Validate AI-generated code and recommendations through engineering review, automated testing, and security checks, using approved tools and protecting sensitive information.
• Define testing and release strategies covering functionality, performance, security, resilience, and compatibility; establish quality evaluations and regression checks for AI capabilities where applicable.
• Own production readiness and service health through service-level objectives, observability, capacity planning, safe deployments, rollback strategies, and incident response.
• Partner with customers, product teams, and engineering stakeholders to prioritize needs, resolve issues, and deliver durable improvements.
• Lead architecture and code reviews, mentor engineers, and influence technical decisions and engineering standards across teams.
• Evaluate emerging technologies through practical experimentation and adopt approaches that demonstrate measurable customer and engineering value.