Overview

hackajob is collaborating with Oracle to connect them with exceptional professionals for this role. Description Role OverviewThe Global Vice President, Compute Control Plane will lead the engineering organization responsible for the core software systems that power the Compute infrastructure portfolio.This leader will own the evolution, scale, reliability, and operational excellence of the common compute platform and the control plane responsible for provisioning, placement, lifecycle management, health management, capacity orchestration, and fleet role requires deep expertise in distributed systems, cloud infrastructure software, control-plane architecture, large-scale service design, and operating mission-critical systems at hyperscale. The GVP will drive architectural consistency, simplify complex systems, improve engineering velocity, and raise the bar on reliability and operational performance.A major component of the role will be using AI transformation to take the platform and engineering organization to the next level. The GVP will drive practical adoption of AI across software engineering, testing, operations, reliability, incident management, fleet orchestration, and engineering productivity.This is both an engineering and business leadership role. The GVP will connect platform and AI-transformation investments to customer experience, capacity availability, cost, efficiency, reliability, and measurable business outcomes. Responsibilities Key Responsibilities Compute Platform Ownership Own the engineering strategy, roadmap, execution, and operational health of the common compute software platform. Drive continued evolution toward greater scale, consistency, modularity, reliability, and operational simplicity. Compute Control Plane Own the architecture, engineering, and operation of the compute control plane across provisioning, scheduling, placement, capacity allocation, lifecycle management, configuration and state management, health monitoring, remediation, fleet orchestration, failure recovery, and service APIs. AI Transformation and Engineering Productivity Drive adoption of AI across the software development and operational lifecycle. Use AI-assisted development tools to improve developer productivity, code quality, testing, debugging, modernization, and engineering velocity. Apply AI to observability, anomaly detection, root-cause analysis, incident response, troubleshooting, automated remediation, capacity forecasting, scheduling, placement, and fleet optimization. Distributed Systems and Platform Architecture Set a high technical bar for distributed systems design across consistency models, state management, concurrency, fault tolerance, partitioning, replication, recovery, idempotency, dependency management, and service isolation. Software Reliability and Operational Excellence Own the operational health and reliability of the compute platform and control plane. Drive continuous reduction in customer-impacting incidents, provisioning failures, service degradation, and operational toil. Advance automated remediation, self-healing systems, and intelligent operational tooling. Scale, Performance, and Efficiency Ensure the compute platform can support continued rapid growth in fleet size, request volume, service complexity, and customer demand. Identify and remove scalability bottlenecks and improve throughput, latency, service efficiency, and software infrastructure cost. Platform APIs and Developer Experience Drive a coherent API and service model across the compute platform. Improve consistency, usability, observability, lifecycle management, tooling, automation, and AI-assisted development capabilities. Capacity and Fleet Orchestration Own the software systems that translate compute capacity into reliable, consumable cloud capacity. Improve discovery, allocation, placement, balancing, onboarding, draining, maintenance, recovery, and lifecycle transitions. Apply AI and predictive analytics to improve forecasti