Senior Capacity Engineer

What you'll do

• Own the end-to-end capacity planning lifecycle including demand forecasting, supply planning, and utilization analysis.
• Build and maintain capacity models that translate business growth signals into specific infrastructure requirements.
• Use AI to build dashboards and reporting tools that surface capacity health, trends, and anomalies.
• Apply AI-driven analysis to predict resource needs and flag capacity risks before they become incidents.
• Identify and drive efficiency improvements through right-sizing, bin-packing, and decommissioning underutilized resources.
• Establish and track capacity-related SLOs and KPIs such as utilization targets and lead times.
• Provide data-driven recommendations for hardware acquisition during procurement and budget cycles.
• Drive quarterly and annual capacity reviews to surface risks and opportunities to senior leadership.

What we're looking for

• 8+ years of experience in infrastructure engineering, capacity planning, SRE, or a related discipline.
• Experience with capacity planning at hyperscale or large enterprise infrastructure.
• Demonstrated experience forecasting infrastructure demand and translating it into actionable supply plans.
• Strong analytical skills for working with large datasets, building models, and drawing conclusions from utilization and performance data.
• Experience applying AI/ML techniques to operational or infrastructure problems like anomaly detection or demand forecasting.
• Ability to build AI-assisted dashboards and tooling that turn raw infrastructure data into actionable insights.
• Solid understanding of compute infrastructure concepts including virtualization, containers, Kubernetes, bare metal, and resource scheduling.
• Ability to communicate complex technical trade-offs to both engineering teams and non-technical stakeholders.
• Hands-on experience with LLMs, AI coding assistants, or generative AI for engineering productivity (preferred).
• Familiarity with infrastructure procurement and hardware lifecycle management (preferred).
• Exposure to time-series forecasting methods, whether statistical or ML-based (preferred).