Network Engineer, Deployment & Support

Meta Meta · Big Tech · Dublin, Ireland

Meta is seeking a Network Deployment & Support Engineer to deploy and support its large-scale global network infrastructure, focusing on implementation, scaling, and troubleshooting. The role involves cross-functional collaboration, technical leadership, vendor management, and documentation, with a requirement for process improvement and automation. While the role is not directly building AI models, it emphasizes integrating AI tools for workflow optimization and adhering to responsible AI practices.

What you'd actually do

  1. Deploy and support our global production including edge, backbone, data center, campus, and content delivery network (CDN) infrastructure
  2. Work cross-functionally with project management, network engineering to generate implementation plans including Method of Procedure (MOP) and Bill of Materials (BOM), Engineering Design Packages (EDP), Scope of Works (SOW), Network equipment and server Rack Face Elevations (RFE)
  3. Troubleshoot and resolve complex network issues, collaborate as needed to ensure timely resolution
  4. Provide technical leadership and guidance during network deployment and migration activities in our data centers and POPs, including in the areas of facility power, cooling, rack layout and cable management, physical equipment installation, network equipment configuration, testing and handoff for production traffic
  5. Drive problem management initiatives to solve complex issues that span multiple organizations. Initiatives exist globally across technical infrastructure reliability, policy, security, compliance, and business strategy

Skills

Required

  • IP networking experience
  • Deploying, migrating and supporting service providers, data center, and/or enterprise network infrastructure
  • Configuring and troubleshooting IP routing/switching technologies and protocols
  • TCP/IP, BGP, ISIS, MPLS, RSVP and SDN
  • Coordinating and executing hardware installation and configuration
  • Enterprise and carrier class routing, switching, and optical transport platforms
  • Data center design and operational best practices
  • Linux OS/networking
  • Analyzing situations and utilizing problem solving experiences under pressure
  • Executing network deployment and/or migration projects
  • Interacting with vendors and external parties
  • Working in a multi-vendor network environment
  • Scripting experience with Python or Perl
  • Process improvement and systems development, leveraging automation to streamline repetitive workflows
  • Integrating AI tools to optimize/redesign workflows
  • Implementing responsible, ethical AI practices
  • Ongoing AI skill development and staying current with emerging AI technologies

Nice to have

  • Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
  • Network certifications such as CCIE, CCNP or JNCIP
  • Up to 10% to 30% of travel

What the JD emphasized

  • 5+ years of IP networking experience with deploying, migrating and supporting service providers, data center, and/or enterprise network infrastructure
  • Experience configuring and troubleshooting IP routing/switching technologies and protocols including TCP/IP, BGP, ISIS, MPLS, RSVP and SDN
  • Hands-on experience with enterprise and carrier class routing, switching, and optical transport platforms
  • Knowledge of data center design and operational best practices
  • Experience with Linux OS/networking
  • Proven experience executing network deployment and/or migration projects
  • Experience interacting with vendors and external parties to drive projects to completion
  • Experience working in a multi-vendor network environment
  • Scripting experience with Python or Perl
  • Network certifications such as CCIE, CCNP or JNCIP
  • Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
  • Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
  • Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies