Technical Program Manager, Network Capacity Planning

Google Google · Big Tech · Bengaluru, Karnataka, India +1

This role focuses on network capacity planning for Google Cloud, utilizing machine learning and AI-driven methodologies to improve forecasting, automate anomaly detection, and simulate capacity scenarios. The Technical Program Manager will ensure network capacity is delivered efficiently and on time to support products and customers, balancing resource efficiency with high availability.

What you'd actually do

  1. Leverage deep knowledge of network fundamentals to understand critical paths, skillfully navigate complex customer requirements, and co-develop tailored solutions alongside Technical Account Managers, Customer Engineers, and Product Managers.
  2. Own the 3–12 month execution window, capturing large agreement requirements and navigating 12+ month network lead times.
  3. Build forecasting models leveraging machine learning and AI to improve predictive accuracy, automate anomaly detection, and simulate capacity scenarios.
  4. Mitigate supplies shortages, maintain optimal inventory levels, manage policy-based buffers, and plan for major peak traffic events.
  5. Collaborate to build AI-assisted automation for ordering and resource fulfillment while designing metrics to track fleet efficiency and demand attainment.

Skills

Required

  • program management
  • infrastructure projects
  • infrastructure designs

Nice to have

  • Data Science teams
  • machine learning
  • AI-driven methodologies
  • forecasting models
  • anomaly detection
  • capacity scenarios
  • network capacity planning
  • demand forecasting
  • inventory optimization
  • supply-demand matching
  • data modeling
  • trend analysis
  • statistics
  • data tools
  • SQL
  • spreadsheet software
  • network infrastructure concepts
  • network routing
  • peering
  • cache infrastructure (CDN)
  • capacity placement
  • large, high-stakes infrastructure initiatives
  • resource efficiency
  • high availability

What the JD emphasized

  • machine learning and AI-driven methodologies
  • forecasting models
  • anomaly detection
  • capacity scenarios
  • AI-assisted automation