Senior AI Infrastructure Engineer, LLM/AI Platforms

CrowdStrike

Senior AI Infrastructure Engineer responsible for designing and building LLM infrastructure for cybersecurity at CrowdStrike. Leading AI-driven security product development while ensuring high standards of performance and reliability.

Posted 7/7/2026full-timeRemote • 🇺🇸 United StatesSenior💰 $140,000 - $215,000 per yearWebsite

About the role

Key responsibilities & impact

Provision and configure large GPU clusters and compute resources for LLM training, finetuning, and inference workloads.
Develop and optimize LLM model-serving infrastructure, including deployment and optimization of various inference frameworks.
Lead model lifecycle management including versioning, checkpointing and reproducibility across training and inference deployments.
Design and champion robust evaluation frameworks to assess model performance, accuracy, and reliability, ensuring AI systems are consistently at production-ready standards.
Identify and address GPU utilization and GPU memory efficiency bottlenecks and apply techniques like quantization, batching, and caching.
Architect and maintain data platforms and pipelines specifically designed to support LLMs, Retrieval-Augmented Generation (RAG), and AI Agentic Systems at scale.
Deliver production-ready code with a focus on performance, maintainability, and testing rigor, ensuring the ability to ship fast without compromising quality.
Apply expertise in data modeling, normalization, and semantic cataloging for AI/ML workloads.
Define and enforce best practices for MLOps/DataOps surrounding LLMs, including monitoring, observability, and zero-touch recovery mechanisms for AI services.
Document architectural designs thoroughly and communicate technical decisions clearly to stakeholders.
Collaborate across the organization with Data Scientists, Product Managers, and other engineering teams to transform research prototypes into robust, production-grade services.

Requirements

What you’ll need

Bachelor’s degree in Computer Science, Data Engineering, or a related STEM field; Master’s degree preferred
6+ years of experience in Infrastructure/Data Engineering, with at least 2 years focused on building and maintaining platforms/pipelines that support LLM-based systems and applications
Demonstrable hands-on experience in LLM infrastructure engineering including cluster provisioning, optimizing training workloads, and maintaining inference pipelines
Exceptional ability to write clean, elegant, performant, and well-tested code, coupled with a strong focus on action and delivering results quickly
Thorough understanding of engineering practices including effective peer code reviews and resilient architecture design
Demonstrates technical leadership and mentorship capabilities
Proven experience utilizing AI technologies to enhance decision-making, streamline workflows and processes, improve efficiency and drive business outcomes.

Benefits

Comp & perks

Market leader in compensation and equity awards
Comprehensive physical and mental wellness programs
Competitive vacation and holidays for recharge
Paid parental and adoption leaves
Professional development opportunities for all employees regardless of level or role
Employee Networks, geographic neighborhood groups, and volunteer opportunities to build connections
Vibrant office culture with world class amenities
Great Place to Work Certified™ across the globe

ATS Keywords

✓ Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

LLM Model-Serving InfrastructurePerformance OptimizationVersioning and CheckpointingQuantization TechniquesData Pipeline ArchitectureClean Code PracticesTesting RigorMonitoring and ObservabilityAI Technologies UtilizationProduction-Ready Code Delivery

Soft Skills

CollaborationCommunicationAction-Oriented MindsetMentorship

Certifications

Bachelor’s Degree in Computer ScienceMaster’s Degree Preferred