Cloud Infrastructure Delivery Lead

Overview

In this role you lead the delivery of AI-ready, hybrid cloud infrastructure for large enterprises, guiding cross-functional teams through end-to-end cloud and infrastructure transformations. You will design GPU-enabled architectures for on-prem, colocation and cloud environments, balancing performance, security and cost. You collaborate with CIO/CTO and AI leadership to shape scalable infrastructure platforms and supervise delivery across consulting, project execution and engineering. This is a mission-critical, client-facing role that combines architecture leadership with hands-on delivery in a fast-paced, AI-driven context.

Pay / Benefits

• Competitive salary
• benefits

Responsibilities

• Design and lead delivery of AI-ready infrastructure platforms for ML, GenAI and HPC workloads
• Define AI compute architectures across on-prem, colocation, and cloud
• Build secure, scalable, cost-efficient GPU platforms for training, fine-tuning and inference
• Lead distributed architecture and engineering teams across consulting, delivery management, and project execution
• Partner with clients to plan, analyse, design, test, and deploy enterprise-scale hybrid cloud transformations

Key requirements

• Strong client-facing consulting experience
• Ability to engage effectively with CIO, CTO and AI leadership
• Excellent written and verbal communication skills
• Experience leading distributed architecture and engineering teams
• Commercial awareness and business case development能力
• Infrastructure design/build/management experience across datacenter, networks, compute, storage
• Cloud services experience (AWS, Azure, GCP)
• Security & resilience, DR/HA/Backup & Recovery
• Virtualization platforms (VMware, Hyper-V, Nutanix)
• Container platforms (OpenShift, EKS, AKS, GKS)
• Infrastructure automation (Terraform, Ansible, Python)
• GPU-based infrastructure design
• NVIDIA AI ecosystem, CUDA and GPU architectures
• AI training environments, AI inference platforms
• HPC infrastructure
• AI workload scheduling
• ML platform architectures

• Client-facing communication
• Stakeholder engagement
• Team leadership and collaboration
• AWS
• Microsoft Azure
• Google Cloud Platform

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