📍 Job Overview

Job Title: Site Reliability Engineer - AI Cloud
Company: Super Micro Computer
Location: Bade, Taoyuan, Taiwan
Job Type: On-site
Category: DevOps Engineer, System Administrator
Date Posted: June 25, 2025
Experience Level: Mid-Senior level (5-10 years)

🚀 Role Summary

Ensure high availability, performance, scalability, and security across GPU-accelerated compute clusters, Kubernetes workloads, and supporting storage/network infrastructure for AI cloud platforms.
Bridge Dev and Ops by automating infrastructure deployment, enhancing observability, and applying SRE best practices to support reliable AI and MLOps environments.
Collaborate with cross-functional teams to maintain and scale GPU clusters, Kubernetes, and AI-optimized storage.

📝 Enhancement Note: This role requires a strong background in Linux, containers, and orchestration, as well as experience managing GPU compute clusters and AI-optimized storage. Familiarity with network protocols and secure multi-tenant environments is also crucial for success in this role.

💻 Primary Responsibilities

Cloud Infra Automation: Design and deploy infrastructure on bare metal or cloud using Terraform, Ansible, or Helm. Automate workflows with Python or Go.
Platform Reliability: Maintain and scale GPU clusters, Kubernetes, and AI-optimized storage (Ceph, BeeGFS, Weka) to ensure stability and performance.
Monitoring & Alerting: Use Prometheus, Grafana, ELK, etc., to monitor system health and trigger alerts on anomalies.
Capacity Planning: Analyze usage patterns and forecast infrastructure needs for AI workloads.
Incident Management: Lead root cause analysis and manage SLOs/SLIs/SLAs to maintain high availability.
CI/CD Integration: Work with DevOps/MLOps teams on CI/CD pipelines using GitLab, ArgoCD, or similar tools.
Security & Compliance: Secure Linux systems, manage certificates, and enforce access controls (RBAC, LDAP SSO, TLS, segmentation).
Documentation & Playbooks: Maintain architecture diagrams, runbooks, and incident playbooks to support knowledge sharing and onboarding.

📝 Enhancement Note: This role involves a high degree of technical complexity, requiring strong problem-solving skills and the ability to work effectively in a collaborative environment. Experience with AI workloads and GPU compute clusters is essential for success in this role.

🎓 Skills & Qualifications

Education: Bachelor's degree in Computer Science, Engineering, or a related field.

Experience: 3-7 years of experience in relevant areas, with a focus on Linux, containers, orchestration, and GPU compute clusters.

Required Skills:

Proficiency in Linux (Ubuntu, RHEL/CentOS)
Containers (Docker, Podman) and orchestration (Kubernetes)
Experience managing GPU compute clusters (NVIDIA / CUDA, AMD / ROCm)
Hands-on experience with observability tools (Prometheus, Grafana, Loki, ELK, etc.)
Strong scripting and coding skills (Bash, Python, or Go)
Familiarity with network protocols, DNS, DHCP, BGP, ROCEv2, and InfiniBand or high-throughput Ethernet fabrics
Excellent collaboration and communication skills for cross-team, partner, and customer initiatives

Preferred Skills:

Experience with Terraform, Ansible, or Helm for infrastructure deployment
Familiarity with secure multi-tenant environments and zero trust architectures
Knowledge of AI-optimized storage (Ceph, BeeGFS, Weka)

📝 Enhancement Note: While not explicitly stated, experience with AI workloads and machine learning environments would be highly beneficial for this role. Additionally, certifications in relevant technologies (e.g., Certified Kubernetes Administrator) could provide a competitive advantage.

📊 Web Portfolio & Project Requirements

Portfolio Essentials:

Demonstrate experience with Linux, containers, and orchestration through relevant projects or case studies.
Showcase your scripting and coding skills with examples of automation scripts or tools you've developed.
Highlight your experience with observability tools and monitoring systems through relevant projects or case studies.
Include examples of your work on GPU compute clusters and AI-optimized storage, if available.

Technical Documentation:

Provide documentation for your projects, including code comments, version control, and deployment processes.
Include any relevant architecture diagrams or runbooks that demonstrate your understanding of complex systems.
Showcase your problem-solving skills by including any incident reports or post-mortem analyses from previous projects.

📝 Enhancement Note: Given the technical nature of this role, a strong portfolio that demonstrates your ability to manage complex systems and automate infrastructure deployment will be crucial for success. Include any relevant projects or case studies that showcase your skills in these areas.

💵 Compensation & Benefits

Salary Range: NT$1,200,000 - NT$1,800,000 per year (Based on market research for mid-senior level DevOps engineers in Taiwan with relevant experience)

Benefits:

Competitive health, dental, and vision insurance plans
Retirement savings plans with company matching
Generous vacation and sick leave policies
Employee discounts on Super Micro Computer products
Opportunities for professional development and training

Working Hours: Full-time, with flexible hours and on-call rotations for 24/7 support

📝 Enhancement Note: While the salary range provided is based on market research, it is essential to verify the actual salary and benefits package with the hiring organization. Additionally, the working hours for this role may vary depending on the specific needs of the team and the organization.

🎯 Team & Company Context

🏢 Company Culture

Industry: Super Micro Computer operates in the technology industry, focusing on server, storage, and networking solutions for data centers, cloud computing, and enterprise IT customers.

Company Size: Super Micro Computer is a large organization with a global presence, employing over 10,000 people worldwide. This size provides opportunities for career growth and exposure to diverse projects and teams.

Founded: Super Micro Computer was founded in 1993 and has since grown to become a leading provider of advanced server, storage, and networking solutions.

Team Structure:

The AI Cloud team consists of a mix of software engineers, DevOps engineers, and system administrators, working collaboratively to deploy, scale, and maintain high-performance AI cloud platforms.
The team follows a flat hierarchy, with a focus on cross-functional collaboration and knowledge sharing.
The team works closely with other departments, including product management, sales, and marketing, to ensure that customer needs are met and that products are delivered on time and within budget.

Development Methodology:

The team follows Agile methodologies, with a focus on iterative development, continuous integration, and continuous deployment.
They use version control systems like Git to manage code and ensure collaboration among team members.
The team employs infrastructure as code (IaC) tools like Terraform and Ansible to automate infrastructure deployment and ensure consistency across environments.
They use monitoring and alerting tools like Prometheus and Grafana to track system health and performance, and to trigger alerts on anomalies.

Company Website: Super Micro Computer

📝 Enhancement Note: Super Micro Computer is a well-established company with a strong focus on innovation and customer satisfaction. The company's size and global presence provide opportunities for career growth and exposure to diverse projects and teams. The AI Cloud team operates in a collaborative and dynamic environment, with a focus on Agile methodologies and continuous improvement.

📈 Career & Growth Analysis

Web Technology Career Level: This role is at the mid-senior level, requiring a strong background in Linux, containers, and orchestration, as well as experience managing GPU compute clusters and AI-optimized storage. The role involves a high degree of technical complexity and requires strong problem-solving skills and the ability to work effectively in a collaborative environment.

Reporting Structure: The Site Reliability Engineer - AI Cloud reports directly to the Manager of AI Cloud Infrastructure. The team works closely with other departments, including product management, sales, and marketing, to ensure that customer needs are met and that products are delivered on time and within budget.

Technical Impact: The Site Reliability Engineer - AI Cloud plays a critical role in ensuring the high availability, performance, scalability, and security of GPU-accelerated compute clusters, Kubernetes workloads, and supporting storage/network infrastructure for AI cloud platforms. Their work directly impacts the reliability and performance of AI and MLOps environments, enabling customers to deploy and scale AI workloads with confidence.

Growth Opportunities:

Technical Growth: As the AI Cloud team continues to grow and evolve, there will be opportunities for the Site Reliability Engineer - AI Cloud to specialize in specific areas, such as GPU compute clusters, AI-optimized storage, or Kubernetes orchestration.
Leadership Growth: With experience and demonstrated success in the role, the Site Reliability Engineer - AI Cloud may have the opportunity to take on a leadership role, managing a team of engineers or serving as a technical lead on critical projects.
Career Transition: Given the company's size and global presence, there may be opportunities for the Site Reliability Engineer - AI Cloud to transition into other roles within the organization, such as software engineering, product management, or technical sales.

📝 Enhancement Note: The Site Reliability Engineer - AI Cloud role offers significant opportunities for technical growth and career advancement. With a strong background in Linux, containers, and orchestration, as well as experience managing GPU compute clusters and AI-optimized storage, individuals in this role can expect to make a significant impact on the reliability and performance of AI cloud platforms. Additionally, the company's size and global presence provide opportunities for career transition and leadership growth.

🌐 Work Environment

Office Type: Super Micro Computer's office in Bade, Taoyuan, Taiwan, is a modern, open-plan workspace designed to foster collaboration and innovation. The office features state-of-the-art technology and ergonomic workstations to ensure the comfort and productivity of employees.

Office Location(s): The office is conveniently located in the Bade District of Taoyuan City, with easy access to public transportation and nearby amenities.

Workspace Context:

The AI Cloud team operates in a collaborative workspace, with a focus on knowledge sharing and cross-functional collaboration.
The team uses a mix of dedicated workstations and shared spaces to accommodate different work styles and preferences.
The office features multiple monitors, testing devices, and other tools to support the development and testing of AI cloud platforms.

Work Schedule: The work schedule for this role is full-time, with flexible hours and on-call rotations for 24/7 support. The team follows a flexible time-off policy, with employees encouraged to take time off when needed to ensure a healthy work-life balance.

📝 Enhancement Note: Super Micro Computer's office in Bade, Taoyuan, Taiwan, provides a modern, collaborative workspace designed to support the development and testing of AI cloud platforms. The office features state-of-the-art technology and ergonomic workstations, with a focus on knowledge sharing and cross-functional collaboration. The flexible work schedule and time-off policy ensure a healthy work-life balance for employees.

📄 Application & Technical Interview Process

Interview Process:

Technical Screening: A phone or video call to assess your technical skills and understanding of Linux, containers, and orchestration. Be prepared to discuss your experience with GPU compute clusters and AI-optimized storage, as well as your familiarity with observability tools and network protocols.
On-site Interview: A visit to Super Micro Computer's office in Bade, Taoyuan, Taiwan, to meet with the AI Cloud team and discuss your fit for the role. You may be asked to complete a technical challenge or case study, demonstrating your ability to manage complex systems and automate infrastructure deployment.
Final Interview: A meeting with the hiring manager or other senior stakeholders to discuss your career goals, expectations, and any remaining questions about the role.

Portfolio Review Tips:

Highlight your experience with Linux, containers, and orchestration through relevant projects or case studies.
Include examples of your work on GPU compute clusters and AI-optimized storage, if available.
Showcase your scripting and coding skills with examples of automation scripts or tools you've developed.
Include any relevant projects or case studies that demonstrate your ability to manage complex systems and automate infrastructure deployment.

Technical Challenge Preparation:

Brush up on your Linux, container, and orchestration skills, with a focus on GPU compute clusters and AI-optimized storage.
Familiarize yourself with the latest developments in observability tools and network protocols.
Prepare for questions about your experience with AI workloads and machine learning environments, if applicable.

ATS Keywords: (Organized by category)

Programming Languages: Bash, Python, Go, Linux shell scripting
Web Frameworks: N/A (This role focuses on backend and infrastructure technologies)
Server Technologies: Linux (Ubuntu, RHEL/CentOS), containers (Docker, Podman), orchestration (Kubernetes), GPU compute clusters (NVIDIA / CUDA, AMD / ROCm), AI-optimized storage (Ceph, BeeGFS, Weka)
Databases: N/A (This role focuses on backend and infrastructure technologies)
Tools: Terraform, Ansible, Helm, Prometheus, Grafana, ELK, Loki, Git, GitLab, ArgoCD, LDAP, TLS, segmentation
Methodologies: Agile, Scrum, infrastructure as code (IaC), continuous integration, continuous deployment, version control, monitoring, alerting, capacity planning, incident management, CI/CD pipelines
Soft Skills: Collaboration, communication, problem-solving, critical thinking, adaptability, attention to detail
Industry Terms: AI cloud platforms, GPU-accelerated compute clusters, Kubernetes workloads, storage/network infrastructure, SRE best practices, AI and MLOps environments, secure multi-tenant environments, zero trust architectures, network protocols, DNS, DHCP, BGP, ROCEv2, InfiniBand, high-throughput Ethernet fabrics

📝 Enhancement Note: The interview process for the Site Reliability Engineer - AI Cloud role is designed to assess your technical skills and cultural fit for the team. By highlighting your experience with Linux, containers, and orchestration, as well as your familiarity with GPU compute clusters and AI-optimized storage, you can demonstrate your ability to manage complex systems and automate infrastructure deployment. Additionally, by including relevant projects or case studies in your portfolio, you can showcase your problem-solving skills and ability to work effectively in a collaborative environment.

🛠 Technology Stack & Web Infrastructure

Frontend Technologies: N/A (This role focuses on backend and infrastructure technologies)

Backend & Server Technologies:

Linux (Ubuntu, RHEL/CentOS)
Containers (Docker, Podman)
Orchestration (Kubernetes)
GPU compute clusters (NVIDIA / CUDA, AMD / ROCm)
AI-optimized storage (Ceph, BeeGFS, Weka)

Development & DevOps Tools:

Infrastructure as code (IaC) tools: Terraform, Ansible, Helm
Version control systems: Git, GitLab
CI/CD pipelines: ArgoCD, GitLab CI/CD
Monitoring and alerting tools: Prometheus, Grafana, ELK, Loki
Network protocols: DNS, DHCP, BGP, ROCEv2, InfiniBand, high-throughput Ethernet fabrics

📝 Enhancement Note: The technology stack for the Site Reliability Engineer - AI Cloud role is centered around Linux, containers, and orchestration, with a focus on GPU compute clusters and AI-optimized storage. The team uses infrastructure as code (IaC) tools to automate infrastructure deployment and ensure consistency across environments. They employ version control systems and CI/CD pipelines to manage code and ensure collaboration among team members. Monitoring and alerting tools are used to track system health and performance, and to trigger alerts on anomalies.

👥 Team Culture & Values

Web Development Values:

Innovation: Super Micro Computer values innovation and encourages its employees to stay up-to-date with the latest developments in AI and machine learning technologies.
Collaboration: The AI Cloud team operates in a collaborative environment, with a focus on knowledge sharing and cross-functional collaboration.
Customer Focus: Super Micro Computer is committed to providing high-quality products and services that meet the needs of its customers.
Performance: The company values performance and encourages its employees to strive for excellence in all aspects of their work.

Collaboration Style:

The AI Cloud team operates in a flat hierarchy, with a focus on cross-functional collaboration and knowledge sharing.
The team uses Agile methodologies to ensure iterative development, continuous integration, and continuous deployment.
They employ infrastructure as code (IaC) tools to automate infrastructure deployment and ensure consistency across environments.
The team uses version control systems and CI/CD pipelines to manage code and ensure collaboration among team members.

📝 Enhancement Note: Super Micro Computer values innovation, collaboration, customer focus, and performance in all aspects of its work. The AI Cloud team operates in a collaborative environment, with a focus on knowledge sharing and cross-functional collaboration. They use Agile methodologies and infrastructure as code (IaC) tools to ensure iterative development, continuous integration, and continuous deployment. The team's flat hierarchy and focus on customer satisfaction ensure that employees are empowered to make a significant impact on the success of the company.

⚡ Challenges & Growth Opportunities

Technical Challenges:

GPU Compute Clusters: Managing GPU compute clusters requires a deep understanding of GPU technologies (NVIDIA / CUDA, AMD / ROCm) and experience with AI workloads.
AI-optimized Storage: Designing and implementing AI-optimized storage solutions (Ceph, BeeGFS, Weka) requires a strong background in storage technologies and experience with AI workloads.
Network Protocols: Familiarity with network protocols, DNS, DHCP, BGP, ROCEv2, and InfiniBand or high-throughput Ethernet fabrics is essential for managing complex AI cloud platforms.
Observability Tools: Experience with observability tools (Prometheus, Grafana, ELK, Loki) is crucial for monitoring system health and performance, and for triggering alerts on anomalies.

Learning & Development Opportunities:

Technical Growth: Super Micro Computer offers opportunities for technical growth through training, mentorship, and project-based learning.
Career Transition: Given the company's size and global presence, there may be opportunities for career transition into other roles within the organization, such as software engineering, product management, or technical sales.
Leadership Development: With experience and demonstrated success in the role, the Site Reliability Engineer - AI Cloud may have the opportunity to take on a leadership role, managing a team of engineers or serving as a technical lead on critical projects.

📝 Enhancement Note: The Site Reliability Engineer - AI Cloud role presents significant technical challenges, requiring a deep understanding of GPU technologies, AI-optimized storage, and network protocols. However, these challenges also provide opportunities for technical growth and career advancement. Super Micro Computer offers opportunities for technical growth through training, mentorship, and project-based learning. Additionally, the company's size and global presence provide opportunities for career transition and leadership development.

💡 Interview Preparation

Technical Questions:

Linux, Containers, and Orchestration: Be prepared to discuss your experience with Linux, containers (Docker, Podman), and orchestration (Kubernetes). Highlight your familiarity with GPU compute clusters and AI-optimized storage.
Observability Tools: Demonstrate your experience with observability tools (Prometheus, Grafana, ELK, Loki) and your ability to monitor system health and performance, and to trigger alerts on anomalies.
Network Protocols: Showcase your familiarity with network protocols, DNS, DHCP, BGP, ROCEv2, and InfiniBand or high-throughput Ethernet fabrics.
AI Workloads: If applicable, discuss your experience with AI workloads and machine learning environments.

Company & Culture Questions:

Company Culture: Research Super Micro Computer's company culture, values, and mission. Be prepared to discuss how your personal values align with the company's and how you can contribute to its success.
AI Cloud Platforms: Familiarize yourself with Super Micro Computer's AI cloud platforms and be prepared to discuss how your technical skills and experience can help drive their success.
Collaboration and Communication: Demonstrate your ability to work effectively in a collaborative environment and to communicate complex technical concepts clearly and concisely.

Portfolio Presentation Strategy:

Technical Depth: Highlight your experience with Linux, containers, and orchestration, as well as your familiarity with GPU compute clusters and AI-optimized storage.
Problem-solving Skills: Include examples of your work on complex systems and your ability to automate infrastructure deployment.
Collaboration and Communication: Showcase your ability to work effectively in a collaborative environment and to communicate complex technical concepts clearly and concisely.

📝 Enhancement Note: The interview process for the Site Reliability Engineer - AI Cloud role is designed to assess your technical skills and cultural fit for the team. By highlighting your experience with Linux, containers, and orchestration, as well as your familiarity with GPU compute clusters and AI-optimized storage, you can demonstrate your ability to manage complex systems and automate infrastructure deployment. Additionally, by including relevant projects or case studies in your portfolio, you can showcase your problem-solving skills and ability to work effectively in a collaborative environment.

📌 Application Steps

To apply for this Site Reliability Engineer - AI Cloud position:

Update Your Resume: Tailor your resume to highlight your experience with Linux, containers, orchestration, and GPU compute clusters. Include any relevant projects or case studies that demonstrate your problem-solving skills and ability to automate infrastructure deployment.
Prepare Your Portfolio: Include examples of your work on complex systems and your ability to automate infrastructure deployment. Highlight your experience with Linux, containers, orchestration, and GPU compute clusters, as well as your familiarity with AI-optimized storage and network protocols.
Research the Company: Familiarize yourself with Super Micro Computer's company culture, values, and mission. Be prepared to discuss how your personal values align with the company's and how you can contribute to its success.
Prepare for Technical Interviews: Brush up on your Linux, container, and orchestration skills, with a focus on GPU compute clusters and AI-optimized storage. Familiarize yourself with the latest developments in observability tools and network protocols. Prepare for questions about your experience with AI workloads and machine learning environments, if applicable.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and web technology industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.

Site Reliability Engineer-AI Cloud