Engineering Manager Transformer Jobs in Riverside, CA

Ingram Micro is seeking a Director, Platform Engineering to own both the day-to-day operational health of our technology platform and the long-term transformation of how we build, release, and run software. This is a dual-mandate leadership role: keep the lights on and evolve what those lights look like.

This role sits within the Global Platform Technology organization, part of the broader CTO organization. The Director will report directly into the Executive Director, Agile Delivery & Program Management, who leads the full cross‑functional technology enablement portfolio alongside Engineering. This structure ensures the role is tightly connected to both engineering execution and the broader operational and delivery agenda.

The Director will lead four core disciplines; SRE, DevOps, Performance Engineering, and Release Management, with a clear, unifying transformation agenda: replace manual processes with intelligent automation, embed AI across every operational function, and build a technology operations organization that is self‑sufficient, preventive, and scalable. The answer to operational complexity is not more people, it is smarter systems.

• RUN: Day-to-Day Operations - Own the operational stability, reliability, and performance of the platform. Ensure SRE, DevOps, release, and performance functions deliver consistently against SLOs, release cadences, and quality benchmarks.
• TRANSFORM: AI & Automation Agenda - Lead the transformation of all four disciplines into AI‑powered, self‑serve functions—where automation handles the routine, AI anticipates the unexpected, and engineers focus on outcomes rather than operations.

• Implement AI‑powered predictive monitoring and anomaly detection to surface degradation signals before they become incidents.
• Build automated remediation, self‑healing infrastructure, AI‑triggered rollbacks, and automated runbook execution, to resolve issues without human intervention.
• Shift the SRE model from reactive firefighting to proactive reliability engineering, using AI insights to eliminate recurring failure patterns.
• Govern SLIs, SLOs, and error budgets as the framework for reliability investment, with real‑time AI forecasting of compliance.
• Embed reliability thinking early in the engineering lifecycle—shifting reliability left, not bolting it on at the end.

• Design and execute an AI‑driven CI/CD strategy targeting 100% touchless, end‑to‑end pipeline automation, zero manual intervention from commit to production.
• Embed AI across the pipeline for intelligent code analysis, automated testing, self‑healing deployments, and anomaly detection.
• Architect and deliver self‑serve developer platforms that empower teams to provision environments, trigger deployments, and manage releases independently.
• Define milestones and metrics for the automation roadmap, tracking progress toward full pipeline autonomy.
• Champion a culture of engineering self‑sufficiency, reducing toil and enabling teams to ship faster with greater confidence.

• Own load, stress, and scalability testing programs, ensuring the platform performs reliably under current and projected demand.
• Automate performance testing as a continuous activity within CI/CD pipelines, blocking releases that regress against defined baselines.
• Use AI to identify performance bottlenecks, forecast capacity needs, and recommend optimizations before issues surface in production.
• Drive FinOps practices to optimize cloud resource utilization and infrastructure spend alongside performance improvements.

• Own the end‑to‑end release process across environments, ensuring consistency, auditability, and minimal disruption to production.
• Automate release orchestration, approval workflows, and deployment coordination to reduce manual overhead and human error.
• Implement AI‑assisted release risk scoring to evaluate change impact, recommend optimal deployment windows, and flag high‑risk releases before they go out.
• Establish feedback loops between release outcomes and development practices to continuously reduce change failure rates.

• Lead a team of leaders and associates spanning SRE, DevOps, performance, and release functions.
• Build a high‑performing, transformation‑minded organization that is as focused on evolving how they work as on the work itself.
• Set clear goals, accountability structures, and transformation milestones across all four disciplines.
• Partner cross‑functionally with engineering, product, and platform teams to ensure operational and delivery excellence.

• Bachelor’s Degree required in Computer Science, Information Technology, Engineering, or related field required; MBA or Master’s degree preferred.
• Minimum of 8‑10 years of relevant experience
• Proven leadership experience across SRE, DevOps, performance engineering, and/or release management at a director level or equivalent.
• A clear track record of transformation, not just running these functions, but fundamentally changing how they operate through automation and AI.
• Hands‑on experience applying AI and ML to operational domains—predictive monitoring, automated remediation, intelligent pipelines, or risk‑based release management.
• Deep expertise in CI/CD design, infrastructure automation, and building self‑serve developer platforms.
• Experience evolving SRE toward prevention‑first, AI‑driven reliability models—including predictive anomaly detection and automated incident resolution.
• Strong understanding of observability principles, SLIs, SLOs, error budgets, logging, metrics, and distributed tracing.
• Experience with cloud infrastructure management, FinOps practices, and cost optimization at scale.
• Excellent stakeholder management and cross‑functional collaboration skills.
• Strong people leadership, with the ability to build and inspire teams through significant organizational change.
• Experience in a large‑scale, global technology organization is a strong advantage.

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