Kubernetes / AI platform Architect
- Compensation
- $120K–$130K / yr
- Location
- Milpitas, California, United States
- Work setup
- Onsite
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 3+ years
- Category
- Architecture
- Posted
Skills
Full description
**Kubernetes / AI platform Architect
Milpitas, CaliforniaJD - Experience**
12+ Years in IT Infrastructure, Platform Engineering, Cloud-Native Technologies, and Enterprise Architecture
5+ Years in Kubernetes Platform Architecture and Operations
3+ Years in Al/ML Platform Design and Enablement
Job Summary
We are seeking a highly experienced Kubernetes / Al Platform Architect to design, implement, and govern enterprise-grade on-premises Al and Kubernetes platforms supporting Generative Al, Machine Learning, Data Science, and cloud- native workloads.
The ideal candidate will lead the architecture of scalable, secure, GPU-enabled Kubernetes environments across private data centers.
Key ResponsibilitiesAl Platform Architecture
Define enterprise Al platform architecture supporting GenAI, LLMS, Machine Learning, and Data Science workloads.
Design GPU-accelerated infrastructure for Al model training, fine-tuning, and inferencing.
Establish scalable Al platform blueprints and reference architectures.
Evaluate and integrate Al frameworks, model serving solutions, vector databases, and inference platforms.
Enable self-service Al environments for data scientists and ML engineers.
Kubernetes Platform Architecture
Design and implement enterprise-grade Kubernetes platforms on- premises.
Define multi-cluster architecture, cluster lifecycle management, and multi- tenancy strategies.
Build highly available, resilient, and scalable container platforms.
Establish Kubernetes standards, governance, security, and operational models
Design platform services supporting modern cloud-native applications.
Platform Engineering & Automation
Architect Infrastructure as Code (laC) and platform automation frameworks.
Implement GitOps practices utilizing ArgoCD, FluxCD, or equivalent technologies.
Automate cluster deployment, patching, upgrades, and configuration management.
Establish CI/CD pipelines supporting application and Al workload deployment.
MLOps & Al Operations
Design MLOps workflows supporting model development, deployment, monitoring, and retraining.
Implement ML lifecycle management and model governance practices.
Enable automated model serving and inferencing pipelines.
Support responsible Al governance and deployment standards.
Security & Compliance
Design secure Kubernetes environments using Zero Trust principles.