This role pays less than
81%
of similar roles. Most pay
$207,310–$287,512
— the shaded band above.
At the midpoint, this role pays about
$191k
versus about
$247k
for comparable roles.
Based on 240 similar postings.
Employer
About Microsoft
Microsoft Corporation is a global technology leader producing software, hardware, and cloud services including Windows, Office 365, Azure cloud platform, Xbox gaming, and Surface devices. Industry: Software & Cloud Computing
Microsoft currently has
644 open roles
on FindRole.
Listed pay typically runs
$119,800–$234,700
across 624 roles with salary data.
Director - AI Capacity Business Development joins the Azure Core & Infrastructure Business Development team to lead external strategy and partnership ecosystems for AI capacity. This role focuses on how the company sources, acquires, and optimizes infrastructure across a landscape including hyperscalers, neocloud providers, GPU-as-a-Service platforms, and datacenter operators. The individual will develop multi-year roadmaps, conduct build, buy, partner, and invest analyses, and lead complex commercial negotiations regarding capacity commitments and infrastructure utilization. Key responsibilities include managing strategic partnerships, evaluating investment opportunities, and influencing executive decision-making on AI workload deployment. Success requires expertise in GPU economics, cluster management, and capacity planning to navigate the technical and financial complexities of scaling global AI services. The role demands proficiency in financial modeling, market analysis, and navigating high-stakes negotiations with CEOs and founders within the semiconductor and cloud infrastructure markets.
Develop multi-year strategic roadmaps for acquiring and managing global AI infrastructure capacity.
Lead complex commercial negotiations and structure agreements for GPUaaS, neoclouds, and other infrastructure providers.
Conduct "build, buy, partner, and invest" analyses to identify and evaluate high-value acquisition and partnership opportunities.
Create frameworks to assess long-term capacity requirements, market trends, and investment priorities.
Influence executive decision-making regarding AI workload deployment and infrastructure optimization.
Manage relationships with key ecosystem players including hyperscalers, datacenter operators, and hardware vendors.
Translate technical and operational data into clear business recommendations for senior leadership.
Establish governance mechanisms to monitor partner performance and manage risks in capacity agreements.
What we're looking for
Bachelor's degree in Business, Engineering, Computer Science, Economics, Finance, Sciences, or a related field.
10+ years of experience in business development, strategy, product management, finance, engineering, consulting, venture capital, private equity, investment banking, corporate development, or related disciplines.
8+ years of experience leading strategic partnerships, commercial negotiations, ecosystem development, or corporate strategy initiatives in the technology sector (preferred).
12+ years of experience in cloud infrastructure, AI, semiconductors, datacenter technologies, networking, or enterprise technology markets (preferred).
Experience with GPUaaS providers, neocloud providers, hyperscalers, AI infrastructure companies, or semiconductor ecosystems (preferred).
Deep understanding of GPU economics, AI training and inference workloads, cluster management, and capacity planning (preferred).
Experience evaluating investment, acquisition, and strategic partnership opportunities (preferred).
Track record of negotiating large-scale commercial agreements and working with CEOs, founders, investors, and executive teams (preferred).
AI Infrastructure
Generative AI
GPU
Python
Machine Learning
Azure
AWS
Google Cloud
Distributed Computing
Cloud Infrastructure
VM
OS
Data Analysis
Technical Program Management
ML Platforms
AI
ML
Azure OpenAI
Microsoft 365 Copilot
Vector Databases
MLOps
CI/CD
Security Automation
Governance as Code
Software Engineering
Data Classification
Vulnerability Management
AI
Machine Learning
Reinforcement Learning
Model Training
AI-for-systems
hardware–software stack
Compiler
Inference
Research Prototyping
Training Pipelines
Performance Optimization