UN
Procurement Specialist - AI Infrastructure
Remote · À temps plein
Soyez le premier à postuler
- Expérience
- 6 ans et plus
- Salaire
- —
- Ouvertures
- 1
- Publié
- il y a 57 minutes
- Mode de travail
- Travaillez à domicile
- CV
- Candidature requise
Description de l'emploi
Overview
We are looking for a highly skilled AI Infrastructure Procurement Specialist with extensive knowledge in hardware sourcing and data center operations. This specialist will spearhead the acquisition and procurement process of compute infrastructure essential for AI workloads, encompassing GPU clusters, networking fabrics, power capacity, and comprehensive supporting components. The ideal candidate will navigate complex supply chains, engage in technical discussions about data center design, and negotiate advantageous commercial agreements involving multi-million dollar contracts.
Key Responsibilities
- Lead strategic sourcing initiatives for GPU systems, CPUs, high-speed networking technologies such as InfiniBand NDR/XDR and 400G/800G Ethernet, as well as high-performance storage.
- Maintain and develop partnerships with OEMs, ODMs, and system integrators, crafting framework agreements and multi-year supply contracts.
- Manage RFP/RFQ processes, implement vendor evaluation scorecards, and conduct competitive bidding across hardware segments.
- Negotiate allocations during supply constraints to secure prioritized product access during launches.
- Handle logistics, timeline coordination, staging processes, and burn-in procedures throughout international supply chains.
- Ensure compliance with export controls and manage cross-border regulations for advanced compute hardware.
- Source and negotiate agreements for colocation, build-to-suit, and powered shell data center spaces with hyperscale, wholesale, and regional operators.
- Assess facility features such as power density (kW per rack), cooling systems (including air, liquid, direct-to-chip, immersion), redundancy tiers (N+1, 2N), power usage effectiveness (PUE), water usage effectiveness (WUE), and sustainability standards.
- Evaluate site selection criteria including latency, fiber connectivity, power reliability, climate considerations, regulations, and future scalability.
- Negotiate commercial conditions covering power pricing, price escalators, SLAs, remote hands services, cross-connections, and termination clauses.
- Develop total cost of ownership (TCO) models covering capital expenditure, power, cooling, networking, real estate, and operational expenses over extended timelines.
- Collaborate cross-functionally with engineering, finance, legal, and sustainability teams to align technical requirements, budgets, and contracts.
- Monitor market trends involving chip roadmaps, lead times, pricing fluctuations, power market dynamics, and emerging data center technologies.
- Support capacity planning and forecasting alongside infrastructure and machine learning engineering leadership.
Qualifications
- A minimum of 6 years' experience in procurement or supply chain management, with at least 2 years specializing in AI infrastructure.
- Proven success negotiating and finalizing multi-million to nine-figure contracts related to hardware and data center services.
- Strong understanding of data center fundamentals including power delivery systems (utility, UPS, PDU, busway), cooling architectures, planning for structural and electrical capacities, and tier classification systems.
- Experience with acceptance testing protocols, validation workflows, and enforcing technical service level agreements (SLAs).
- Competency in TCO modeling, scenario analysis, and structuring financial arrangements for infrastructure acquisitions.
- Excellent commercial acumen and negotiation capabilities, with the aptitude to bridge communication between engineering, finance, and vendor teams.
Preferred Attributes
- Experience procuring hardware and services across multiple international regions.
- Established connections in the AI hardware ecosystem, including relationships with NVIDIA, hyperscalers, neocloud providers, and top OEM/ODMs.
- Background in engineering disciplines such as computer science, electrical engineering, or other quantitative fields.
Compétences
Collaboration interfonctionnelle
Coordination logistique
Négociation de contrat
Gestion de la chaîne d'approvisionnement
Market Intelligence
Planification des capacités
Data Center Operations
Vendor Relationship Management
hardware sourcing
AI infrastructure procurement
total cost of ownership modeling
technical SLA enforcement