AI Infrastructure Engineer
Job Location:
Job Location:
San Jose, CA - USA
Monthly Salary:
Not Disclosed
Monthly Salary:
Posted on:
20 days ago
Posted on:
Vacancies:
1 Vacancy
Vacancies:
Job Summary
Project Role: AI Infrastructure Engineer
Location: San Jose CA
Duration: 6 months
Role Description: Architect and build custom Artificial Intelligence (AI) infrastructure solutions
leveraging the Nutanix Kubernetes Platform and Nutanix AI. You will be responsible for
designing high-performance computational stacks that integrate Nutanix AI high-speed
software-defined storage and GPU-accelerated nodes. Your mission is to make AI infrastructure
& ;invisible &; by optimizing for performance power consumption and seamless hybrid-multicloud
scalability across on-prem.
Must-Have Skills: * Nutanix Cloud Infrastructure (NCI) & AOS/AHV
NKP/NAI (specifically NKP - Nutanix Kubernetes Platform)
Knowledge of NVIDIA and AMD Ecosystem
Minimum Experience: 10 years Educational Qualification: 12 years full-time education
Summary
As an AI Infrastructure Engineer you will design tailored AI solutions that bridge the gap
between private data centers and public cloud. Your day-to-day will involve optimizing the
Nutanix computational stack for large language models (LLMs) and generative AI workloads.
You will serve as the SME for Nutanix AI ensuring that compute storage (Nutanix
Objects/Files) and networking (Flow) are perfectly tuned for AI model training and inference.
As an AI Infrastructure Engineer you will design tailored AI solutions that bridge the gap
between private data centers and public cloud. Your day-to-day will involve optimizing the
Nutanix computational stack for large language models (LLMs) and generative AI workloads.
You will serve as the SME for Nutanix AI ensuring that compute storage (Nutanix
Objects/Files) and networking (Flow) are perfectly tuned for AI model training and inference.
Nutanix-Specific Responsibilities
Hybrid Multicloud Architecture: Design seamless AI workflows using NC2 on Prem
allowing for rapid bursting of AI workloads from on-prem AHV clusters to the public
cloud.
Data Services for AI: Architect high-performance storage backends using Nutanix
Objects (S3-compatible) to handle the massive datasets required for AI/ML.
Kubernetes & Orchestration: Deploy and manage AI workloads using Nutanix
Kubernetes Platform (NKP) to ensure containerized AI models are scalable and
resilient.
Infrastructure-as-Code: Implement IaC using Nutanix Calm or Terraform to automate
the lifecycle of GPU-enabled nodes.
Observability: Design frameworks (monitoring logging alerting) for proactive issue
detection. Hands on experience on Prometheus Grafana ELK and OpenTelemetry.
Ensure high availability disaster recovery and fault tolerance across all systems.
Networking & Security: Familiarity with Zero-Trust architectures enterprise
networking storage and virtualization.
Invisible Infrastructure: Modernize legacy 3-tier AI silos into a unified web-scale
Nutanix environment.
Public
Professional & Technical Skills
Nutanix Core: Deep proficiency in AOS (Acropolis Operating System) and AHV
(Native Hypervisor).
AI Performance: Experience with GPU Passthrough and vGPU configurations on
Nutanix to optimize AI training performance.
Security: Applying Nutanix Flow for micro segmentation to secure sensitive AI training
data.
Cost Management: Using Nutanix Cloud Manager (NCM) Cost Governance to
monitor and optimize spend across hybrid environments.
The "Hungry Humble Honest &; Expectations
SME Leadership: Act as the primary technical authority for Nutanix AI integrations
within the San Jose office.
Collaboration: Work across teams to dismantle data silos moving the organization
toward a "One Platform" philosophy.
Strategic Vision: Stay ahead of Nutanix product roadmaps to inform long-term AI
infrastructure strategy.
Professional & Technical Skills
Nutanix Core: Deep proficiency in AOS (Acropolis Operating System) and AHV
(Native Hypervisor).
AI Performance: Experience with GPU Passthrough and vGPU configurations on
Nutanix to optimize AI training performance.
Security: Applying Nutanix Flow for micro segmentation to secure sensitive AI training
data.
Cost Management: Using Nutanix Cloud Manager (NCM) Cost Governance to
monitor and optimize spend across hybrid environments.
The "Hungry Humble Honest &; Expectations
SME Leadership: Act as the primary technical authority for Nutanix AI integrations
within the San Jose office.
Collaboration: Work across teams to dismantle data silos moving the organization
toward a "One Platform" philosophy.
Strategic Vision: Stay ahead of Nutanix product roadmaps to inform long-term AI
infrastructure strategy.
