below marketmost similar roles pay hereabove market
This role pays more than
99%
of similar roles. Most pay
$168,750–$261,950
— the blue band above.
At the midpoint, this role pays about
$362k
versus about
$215k
for comparable roles.
Based on 240 similar postings.
Employer
About CrowdStrike
CrowdStrike is a leading American cybersecurity technology firm, specializing in cloud-native endpoint protection, threat intelligence, and incident response.
CrowdStrike currently has
18 open roles
on FindRole.
Listed pay typically runs
$132,500–$197,500
across 18 roles with salary data.
The Vice President, AI Architect will join the team to define and drive a consistent, modern AI- and agentic-first architecture across the entire company. This role involves connecting AI applications with software and infrastructure at scale, translating business priorities into practical designs, and establishing common technical foundations for shared platforms and customer-facing products. Key responsibilities include designing full-stack AI and agentic systems, benchmarking model serving options, and developing production reference implementations. The architect will manage the AI lifecycle, including data ingestion, model adaptation, evaluation, and operational feedback. Required skills include deep expertise in distributed systems, software design, APIs, and performance engineering. The role addresses the technical challenge of building resilient, secure, and scalable AI systems that integrate models with retrieval, memory, and execution environments across cloud and edge environments.
Define and drive the company-wide AI architecture and roadmap across all business units.
Establish shared interfaces, design patterns, and common technical foundations for AI applications.
Design and build full-stack AI and agentic systems including models, retrieval, and execution environments.
Architect production AI systems for performance, resilience, scale, and low latency.
Develop prototypes and production reference implementations to provide a tested foundation for engineering teams.
Design the AI lifecycle architecture from data ingestion through evaluation and operational feedback.
Embed security, governance, and auditability into AI architectures to prevent data exposure and prompt injection.
Create a consistent developer experience through reusable components, common APIs, and shared tooling.
What we're looking for
BS, MS, or PhD in computer science, computer engineering, or a related technical field, or equivalent practical experience.
10+ years of experience in software architecture, systems engineering, or AI engineering.
Senior technical leadership experience across multiple products or business units.
Proven record of personally designing and delivering production AI systems, including generative or agentic applications and supporting infrastructure.
Strong foundations in distributed systems, software design, APIs, and performance engineering across application and infrastructure layers.
Practical experience with model integration, inference serving, retrieval, and evaluation for AI systems at scale.
Experience establishing shared platforms and engineering standards across independent teams.
Ability to align senior stakeholders, explain architectural tradeoffs, and mentor engineers through implementation.
Significant contributions to open-source projects or industry standards (preferred).
Depth in Linux systems software, networking, accelerated computing, or AI deployment across cloud and edge environments (preferred).
Experience modernizing established platforms into AI-native systems, particularly in cybersecurity (preferred).
Generative AI
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API Architecture
CI/CD
DevSecOps
Platform Engineering
Model Serving
Prompt Orchestration
Agentic AI
IAM
Observability
Generative AI
LLMs
RAG
AWS
Azure
Google Cloud Platform
Vector Databases
Distributed Systems
Microservices
API Architecture
CI/CD
DevSecOps
Platform Engineering
Model Serving
Prompt Orchestration
Agentic AI
IAM
Observability
Generative AI
Machine Learning
LLMs
RAG
AWS
Azure
Google Cloud Platform
Vector Databases
Microservices
Distributed Systems
Event-Driven Architecture
API Architecture
DevSecOps
Model Governance
System Integration
Cloud-Native Services
Generative AI
Machine Learning
LLMs
RAG
AWS
Azure
Google Cloud Platform
Vector Databases
Microservices
Distributed Systems
Event-Driven Architecture
API Architecture
DevSecOps
Model Governance
System Integration
Cloud-Native Services
Generative AI
Machine Learning
LLMs
RAG
AWS
Azure
Google Cloud Platform
Vector Databases
Microservices
Distributed Systems
Event-Driven Architecture
API Architecture
DevSecOps
Model Serving
System Integration
Cloud-Native Services
Responsible AI
Model Governance