Sr Domain\\Technical Architect

Sapsf; United States; On-site; USD 140,000 - 170,000; Full time

Job summary

Sapsf seeks an experienced AI Factory Platform Operations Engineer to support deployment, operations, troubleshooting, and reliability of enterprise AI infrastructure. You will manage Linux-based infrastructure, Kubernetes, container platforms, networking, automation, and observability across AI/GPU environments.

Collaborate with infrastructure, network, DevOps, cloud, security, and engineering teams to maintain availability, perform root cause analysis, and improve operational reliability.

Qualifications

• Proficient Linux administration experience (RHEL/ Rocky/ Ubuntu).
• Kubernetes production experience including architecture, networking, storage, RBAC, upgrades.
• Docker/container runtime knowledge and troubleshooting skills.
• Strong networking fundamentals: TCP/IP, DNS, routing, load-balancing.
• Shell/Bash scripting and log analysis for operations.
• Experience with incident management and root cause analysis (RCA).
• Experience with infrastructure monitoring and observability tools.
• Version control with Git and collaboration workflows.
• Infrastructure-as-Code tooling (Ansible/Terraform) and cloud exposure.

Responsibilities

• AI/GPU infrastructure operations and support for enterprise platforms.
• Perform health checks and troubleshooting to ensure platform reliability.

Skills

Linux administration

Kubernetes administration

Docker/container runtime

Networking fundamentals

Scripting (Shell/Bash)

Performance analysis

Incident management/RCA

Git/version control

Infrastructure monitoring

Tools

Kubernetes

Docker

Prometheus

Grafana

Splunk

Datadog

Dynatrace

Ansible

Terraform

Job description

Career Opportunities: Sr Domain\Technical Architect (182450)

Requisition ID182450-Posted10/16/2026

We are seeking an experienced AI Factory Platform Operations Engineer to support deployment, operations, troubleshooting, and reliability of enterprise AI infrastructure. The role will focus on Linux-based infrastructure, Kubernetes and container platforms, networking, automation, monitoring, and operational support of AI/GPU environments. The engineer will work closely with infrastructure, network, DevOps, cloud, security, and engineering teams to maintain platform availability, resolve complex infrastructure issues, perform root cause analysis, and improve operational reliability.

Key Responsibilities

AI / GPU Infrastructure Operations Support day-to-day operations of AI infrastructure and GPU-based platforms. Perform infrastructure health checks,

Skill Requirements

Strong Linux administration: RHEL / Rocky Linux / Ubuntu Kubernetes administration and troubleshooting Docker / container runtime knowledge TCP/IP, DNS, routing, switching and load-balancing fundamentals Shell/Bash scripting Linux performance and log analysis Incident management and RCA Git/version control Infrastructure monitoring and troubleshooting The existing JD specifically calls for production Kubernetes experience including architecture, networking, storage, RBAC, pod lifecycle and cluster upgrades.

Other Requirements

AI Infrastructure / GPU platform exposure NVIDIA/GPU infrastructure concepts Prometheus / Grafana / Splunk / Datadog / Dynatrace Ansible / Terraform AWS / Azure / GCP CI/CD and DevOps practices Infrastructure-as-Code Experience working in large-scale production environments