AI Infrastructure Engineer
About The Position
We are seeking an AI Infrastructure Engineer to build, operate, and continuously enhance the Linux and GPU-based infrastructure that powers our AI platforms and performance testing environments. This is a highly hands-on infrastructure, automation, and performance engineering role. You will design and maintain benchmarking frameworks, ensure the reliability and security of lab and compute environments, and develop internal AI platform services that support customer demonstrations, proof-of-concepts, competitive evaluations, and technical sales efforts. Your work will play a key role in helping secure new customer opportunities by enabling rapid validation of customer requirements and showcasing the performance and capabilities of our AI solutions.
Requirements
- Hands-on Linux systems administration and infrastructure experience (servers, networking fundamentals, virtualization).
- Experience with KVM or containerization (Docker/Kubernetes).
- Familiarity with DevOps toolchains (CI/CD, IaC, monitoring/observability stacks).
- Strong scripting/automation ability in Python and/or Bash, applied to infrastructure or testing workflows.
- Practical experience with GPU-based compute environments or AI/ML infrastructure.
Nice To Haves
- Bachelor's degree in Computer Science, Computer Engineering, or a related field (equivalent experience welcome).
- Experience with performance testing, benchmarking, or traffic-generation methodologies.
- Exposure to network security concepts and enterprise security technologies.
- Experience with Fortinet products (FortiGate, FortiManager, FortiAnalyzer) or similar enterprise security/networking solutions — a plus, not required.
- Experience in customer-facing technical work: proof-of-concept support, demos, or technical presentations.
- Experience creating technical documentation, white papers, or delivering internal training.
Responsibilities
- Operate, maintain, and enhance automated performance testing services that benchmark throughput, latency, scalability, and resource utilization.
- Develop new testing frameworks, automation, and validation methodologies as requirements evolve.
- Analyze performance data and recommend improvements to reliability, scalability, and operational efficiency.
- Deploy, monitor, and optimize Linux servers, GPU clusters, virtualization platforms, and lab hardware.
- Support availability and utilization of compute resources for engineering, testing, and AI workloads.
- Evaluate and integrate new hardware technologies to improve performance and efficiency.
- Implement monitoring, logging, alerting, failover, and incident response for critical infrastructure.
- Develop, deploy, and maintain internal AI server applications and shared compute platforms.
- Improve operational efficiency through automation and proactive issue identification; troubleshoot complex system/network/application issues.
- Support day-to-day lab operations: access control, network administration, resource allocation, and capacity planning.
- Contribute to operational standards and best practices for lab governance.
- Create and maintain technical documentation: benchmarking reports, design notes, and internal runbooks.
- Share technical insights and best practices with the broader engineering organization.
Benefits
- medical
- dental
- vision
- life and disability insurance
- 401(k)
- 11 paid holidays
- vacation time
- sick time
- comprehensive leave program
- Fortinet equity program
- commissions based on the terms of the Sales Compensation Plan
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
Associate degree
