Tencent Cloud- MaaS Compute Resource Operations Manager (GPU Infrastructure Delivery)

• Resource Planning and Supply Strategy: Forecast GPU compute demand based on MaaS business growth, model evolution and resource utilization. Develop resource portfolio, supply scale and delivery plans that balance cost, efficiency and supply stability.
• Procurement Collaboration and Delivery Assurance: Participate in GPU compute procurement. Work with procurement and technical teams to define resource specifications, service standards and acceptance criteria; assess supplier delivery capabilities; and track resource arrival, service launch and issue resolution.
• Supply Security and Risk Management: Monitor market supply and demand, supplier capacity and regulatory compliance risks. Develop multi-supplier, cross-region resource assurance plans, plan capacity reservations and buffers, and reduce the risk of supply interruption and idle capacity.
• Technical Collaboration and End-to-End Acceptance: Work with technical teams on GPU selection and the evaluation of supporting data center, network and storage solutions. Drive end-to-end delivery, performance testing and acceptance to ensure resources meet model deployment and inference service requirements, including on-site inspections at supplier facilities.

Requirements

• 10+ years of experience in large-scale IT infrastructure, compute resource delivery, sourcing or supply chain management, with an end-to-end track record in resource planning, procurement collaboration and delivery management.
• Professional proficiency in English & Mandarin, as approximately 50% of the role involves communicating with, reporting to and presenting to Mandarin-speaking stakeholders at our headquarters.
• Familiarity with the GPU supply landscape, including supplier and channel systems for NVIDIA, AMD and other mainstream products, and the ability to assess resource availability, delivery lead times and supply risks.
• Willingness to travel approximately 50% across Southeast Asia, Northeast Asia and Central Asia, including on-site stays of up to two weeks during delivery and acceptance.
• Familiarity with GPU servers, data centers, networks and storage, and an understanding of the deployment conditions, delivery processes and acceptance standards for large-scale compute clusters.
• Understanding of large-model deployment and inference services, including token metering, billing methods and key metrics such as throughput, latency and concurrency, with the ability to translate business requirements into resource and cost estimates.
• Strong data analysis, cost modeling and cross-team collaboration skills, with the ability to coordinate business, technical, procurement and supplier stakeholders to drive projects to completion.
• Experience in compute resource operations, compute procurement or large-scale GPU cluster delivery at a cloud provider, GPU cloud provider or leading AI / large-model company is preferred.