Director - AI Capacity Business Development

About the role

Develop a deep understanding of AI infrastructure markets, capacity constraints, emerging technologies, customer demand signals, and indusrty dynamics. AI Capacity Strategy Create and maintain a comprehensive view of the global AI infrastructure landscape, including hyperscalers, neoclouds, GPUaaS providers, model companies, datacenter operators, infrastructure startups, and hardware vendors.

Evaluate capacity supply, technology trends, cost structures, performance characteristics, and partner capabilities to inform executive decision making. Develop frameworks for assessing long-term capacity requirements, market opportunities, and infrastructure investment priorities. Manage relationships with strategic AI infrastructure partners and emerging ecosystem players.

Establish and execute multi-year partnership roadmaps with key ecosystem participants. Evaluate opportunities for strategic partnerships, commercial relationships, investments, and ecosystem development. Analyze industry dynamics and emerging business models across the AI infrastructure landscape.

Build, Buy, Partner, and Invest Analysis Develop executive-level recommendations supported by market analysis, financial modeling, and market intelligence. Establish governance mechanisms for strategic partnerships and capacity agreements. Ensure agreements deliver intended business outcomes and strategic value. Develop executive reporting and performance management processes.

Contribute to the growth and development of the Azure Core & Infrastructure Business Development organization. Lead highly complex partnership discussions and commercial negotiations. Structure agreements involving capacity commitments, infrastructure utilization, strategic investments, joint development initiatives, and ecosystem partnerships.

Drive alignment across Finance, Engineering, Supply Chain, CELA, Security, Product, and Executive stakeholders. Develop creative deal structures that balance strategic flexibility, economics, risk management, and long-term value creation. Lead transactions from opportunity identification through final execution and implementation. Develop decision frameworks that help leadership navigate complex AI capacity tradeoffs.

Translate technical and operational considerations into clear business recommendations. Lead cross-functional initiatives spanning Azure, AI Platform, Infrastructure, Finance, Supply Chain, and Corporate Strategy organizations. Build alignment among diverse stakeholders and drive decisions in highly ambiguous environments. Mentor and develop members of the broader Business Development organization.

Bachelor's Degree in Business, Engineering, Computer Science, Economics, Finance, Sciences, or related field AND 10+ years of relevant experience in business development, strategy, product management, finance, engineering, consulting, venture capital, private equity, investment banking, corporate development, or related disciplines; OR equivalent experience.

8+ years of experience leading strategic partnerships, commercial negotiations, ecosystem development, or corporate strategy initiatives in the technology sector. Experience influencing executive-level decision making and driving strategic outcomes across large organizations. 12+ years of experience in cloud infrastructure, AI, semiconductors, datacenter technologies, networking, or enterprise technology markets.

Direct experience with GPUaaS providers, neocloud providers, hyperscalers, AI infrastructure companies, or semiconductor ecosystems. Deep understanding of GPU economics, AI training and inference workloads, cluster management, and capacity planning. Experience evaluating investment, acquisition, and strategic partnership opportunities. Track record of negotiating and executing large-scale commercial agreements.

Experience working directly with CEOs, founders, investors, and executive teams. Proficient financial modeling, market analysis, and strategic planning capabilities. Understanding of emerging AI infrastructure architectures, capacity optimization techniques, and evolving AI deployment models.