Head of Practice Management – AI Data Centers, Infrastructure

Full Description

About Magna AI:Magna AI is a global integrated-value-chain AI transformation factory, architecting the future of the intelligent enterprise.

Through a unified approach that spans strategy, engineering, integration, and operations, Magna AI delivers secure AI infrastructure, applications, and services designed to drive measurable, scalable, and organization-wide transformation.

Powered by next-generation technology from Trend Micro, NVIDIA, and Wistron Digital Technology Holding Company, Magna AI enables enterprises to evolve into intelligent, adaptive, and future-ready organizations confidently.

Magna AI enables enterprises to evolve into intelligent, adaptive, and future-ready organizations confidently.

Building the enterprise AI economy.

Job Summary:Own, build, and scale theAI Data Center, AI Factory, Infrastructure, and Cloud Practiceas afull-lifecycle, revenue-generating business unit.

Accountable forend-to-end ownershipacross business case creation, design, build, deployment, and operations ofAI-optimized physical and cloud infrastructure, includingsovereign AI factories, enterprise AI data centers, and hybrid cloud platforms.

This role governswhat we sell, how we design it, how we build it, how we operate it, and how we monetize it.

Practice Scope (Explicit):AI Data Centers & AI Factories (Physical Layer)Full Lifecycle OwnershipBusiness Case & Feasibility:CAPEX/OPEX, ROI, IRR, payback • AI workload demand modeling (training vs inference, burst vs steady) • Power, land, water, cooling feasibility • Sovereignty, residency, regulatory assessments • Phased scale strategy (pilot → scale → hyperscale)Scoping, Survey & Site Analysis:Site selection & due diligence • Grid & power interconnection • Environmental, thermal, seismic analysis • Network proximity & latency • Rack density & power planningDesign & Engineering (MEP):High-density power (30–120kW+ per rack) • Liquid/immersion/hybrid cooling • Redundancy (N+1, 2N, distributed resilience) • Electrical systems (substations, UPS, generators) • CDU, chilled water, direct-to-chip • Fire, safety, physical security • Modular/containerized AI factoriesConstruction & Commissioning:EPC governance • Build sequencing • FAT / SAT • IST • AI-specific commissioning & burn-inOperations & Lifecycle:AI DC operating models • Capacity expansion • Predictive/preventive maintenance • Energy efficiency & PUE • SLA governance • AI hardware refresh cyclesB.

Infrastructure Stack (Inside the AI Data Center)End-to-End OwnershipCompute:GPU/accelerator strategy • Multi-vendor architectures • Bare metal vs virtualized • Kubernetes, Slurm alignmentStorage:High-throughput AI storage • Object/block/file • Tiered training pipelines • Data locality optimizationNetworking:400G/800G fabrics • East-west optimization • RDMA / InfiniBand / Ethernet • Multi-site connectivityRacks & Physical:AI-dense layouts • Weight, airflow, cabling • Future-proofingPlatform Integration:AI orchestration • Scheduling & isolation • Monitoring, telemetry, observabilityC.

Cloud Platforms (Hyperscale & Hybrid)AWS:AI/ML services • Hybrid connectivity • Cost optimization & reserved capacity • Security & complianceMicrosoft Azure:Enterprise AI workloads • Hybrid via on-prem AI factories • Identity, security, governanceGoogle Cloud Platform:Data & AI pipelines • Multi-cloud portabilityHybrid & Sovereign Cloud:On-prem AI factories • Sovereign architectures • Cross-cloud orchestrationPractice Management ResponsibilitiesPractice Architecture & Offerings:AI Factory advisory • Design & build • Infrastructure deployment • Hybrid cloud enablement • Managed operations • Reference architectures & repeatable assetsCommercial & P&L Ownership:Revenue, margin, utilization • Pricing for AI factories, infrastructure builds, cloud & managed services • Large-scale AI deal shapingDelivery Governance:Lifecycle governance • Quality, risk, compliance • Executive escalation for mission-critical programsTalent & Capability Building:AI DC design • Infrastructure engineering • Cloud architecture • Operations • Competency models & certificationsVendor & Partner Ecosystem:GPU vendors • OEMs • EPCs • Hyperscalers • Sovereign cloud partners • Technology validation & standardizationRequired Profile15+ years in data centers, infrastructure, and cloud with AI/HPC exposure • Leadership in AI or hyperscale environments • Design, build, and operate experience • Strong commercial ownership & executive presence • Deep understanding of AI workload physical implications