Principal Technical Project Manager

About the role

We are seeking a Principal Technical Project Manager to lead the strategic planning, evaluation, and delivery of overseas AI cloud resources. You will work closely with algorithm and engineering teams to translate AI model requirements into robust infrastructure solutions that span multiple regions and cloud providers.

Key responsibilities

• Track market dynamics of overseas AI cloud computing and large‑model services, delivering technology trend assessments and strategic insights.
• Conduct performance benchmarking and cross‑vendor analysis of GPU compute platforms, and evaluate network architecture and high‑performance storage solutions.
• Translate model training and inference requirements into technical specifications and design multi‑region, multi‑GPU resource pool scheduling.
• Own end‑to‑end delivery of cloud resource projects, managing cross‑regional, multi‑vendor delivery cadence and ensuring SLA compliance under high‑concurrency demand.
• Build compute cost allocation models (TCO) and drive cost reduction through resource scheduling optimization and reserved capacity strategies.

Required profile

• Bachelor’s degree or higher in Computer Science, Electronic Engineering, or a related field.
• 10+ years of experience in infrastructure or AI‑infra domains, with at least 3 years leading overseas cloud resource programs.
• Deep familiarity with the GPU compute ecosystem and supply chains of major cloud providers (AWS, GCP, Azure, CoreWeave, etc.).
• Proven track record of designing and delivering large‑scale AI infrastructure solutions.
• Excellent program management and coordination skills across vendors, stakeholders, and technical teams.
• Fluent in English and Chinese, both spoken and written, with experience in overseas work cultures.

Required skills

• AWS
• Google Cloud Platform (GCP)
• Microsoft Azure
• CoreWeave
• GPU compute platforms
• Network architecture design
• High‑performance storage solutions
• Cost allocation modeling (TCO)
• Resource scheduling optimization