● Translate demand into infrastructure requirements. Work with AI business and platform teams to define compute capacity, network bandwidth and latency, connectivity, data access, scalability, availability, and operational needs. Convert these into supplier requirements, evaluation criteria, delivery milestones, and acceptance criteria.
● Lead network design and delivery reviews. Review data centre fabric, inter-data-centre and WAN connectivity, routing, IP planning, network segmentation, resilience, and expansion plans with internal architects and suppliers. Ensure designs address both AI workload requirements and day-to-day operability.
● Manage network providers and delivery dependencies. Coordinate network equipment vendors, carriers, colocation partners, and system integrators on circuits, cross-connects, cabling, configuration handoffs, implementation windows, and fault escalation. Track responsibilities and resolve gaps between suppliers.
● Drive data centre readiness. Coordinate rack space, power, cooling, physical access, cabling, carrier connectivity, and installation prerequisites. Identify site or network dependencies that could delay cluster deployment.
● Own integration and acceptance governance. Coordinate compute, network, storage, and platform teams through installation, integration, testing, remediation, and sign-off. Require evidence for connectivity, bandwidth, latency, failover, and agreed cluster-level outcomes—not just confirmation that equipment has been installed.
● Manage delivery and service accountability. Maintain integrated plans, risks, decision logs, and supplier action trackers. Define handover documentation, monitoring expectations, incident escalation, change procedures, support boundaries, and service review cadence.
● Communicate technical risks clearly. Explain design trade-offs, unresolved defects, capacity constraints, and schedule impacts to both engineering teams and business stakeholders.