Devops Enigneer/Devops运维

Core Responsibilities

• Design, build, and maintain AWS cloud infrastructure and private data center environments using Infrastructure as Code (IaC).

• Manage large-scale Kubernetes (EKS) clusters, including cluster deployment, upgrades, scaling, networking (CNI), storage management, and operational maintenance.

• Develop internal DevOps platforms, automation tools, and command-line utilities using Python or Go to improve engineering productivity and operational efficiency.

• Build and maintain end-to-end monitoring and observability platforms based on Prometheus, Grafana, and ELK Stack to ensure system reliability and rapid troubleshooting.

• Manage AI infrastructure, including GPU servers (NVIDIA A100, T4, etc.), CUDA environments, driver versions, and GPU resource allocation.

• Deploy, maintain, and optimize containerized AI applications such as ComfyUI and other Generative AI services for high concurrency and production environments.

Requirements

Education

• Bachelor''s degree or above in Computer Science, Information Technology, or related disciplines.

Experience

• Minimum 3 years of experience in DevOps, Site Reliability Engineering (SRE), or Infrastructure Engineering.

Languages

• Fluent in Mandarin (spoken and written).

Technical Skills

Cloud & Containers

• Strong experience with AWS services, including EC2, EKS, S3, VPC, IAM, and related cloud infrastructure.

• Deep understanding of Kubernetes architecture, scheduling, networking, storage, and container orchestration.

Programming

• Strong programming skills in Python or Go.

• Experience developing backend services, automation tools, or internal DevOps platforms.

System Administration

• Strong knowledge of Linux operating systems.

• Familiarity with TCP/IP, HTTP, DNS, Shell scripting, and system troubleshooting.

CI/CD

• Hands-on experience with Jenkins, GitLab CI/CD, GitHub Actions, or similar continuous integration and deployment platforms.

Preferred Qualifications

GPU Infrastructure

• Experience managing large-scale GPU clusters.

• Knowledge of GPU monitoring, resource scheduling, memory optimization, and Spot Instance cost optimization.

Generative AI Infrastructure

• Hands-on experience deploying and maintaining ComfyUI, Stable Diffusion WebUI, or similar AI inference platforms.

• Experience with dependency management, multi-user concurrency optimization, and AI model loading acceleration.

MLOps

• Familiarity with Kubeflow, MLflow, Triton Inference Server, or similar MLOps platforms.

High Performance Computing

• Experience with RDMA networking, distributed computing, and large-scale parallel processing environments.