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Job Post Details

AI Data Center Instructor – LLMOps, GPU & Kubernetes - job post

WeCloudData
4.7 out of 5 stars
Remote
Full-time, Contract

Job details

Job type

  • Contract
  • Full-time

Full job description

About Us

WeCloudData is a premier learning academy dedicated to offering top-quality data and AI training to our students and corporate clients. Recognized as a leading school in the data and AI category, we have expanded our offerings to include consultancy and career services, supporting our students beyond the classroom. Over the next decade, our goal is to influence millions of learners, driving positive changes in the future of work through our commitment to excellent learning experiences, technological innovation, and fostering work-integrated learning environments. We pride ourselves on setting industry standards and being at the forefront of data and AI education.

Job Description

WeCloudData is seeking an experienced AI Data Center Instructor to deliver a 6-weeks hands-on bootcamp focused on GPU infrastructure, Docker, Kubernetes, LLMOps, AIOps, and AI workload operations.

We need both onsite and online instructors for this program. This program is designed to help students understand how modern AI workloads are deployed, operated, monitored, and supported in data center or cloud environments. The role is not focused on AI model research or algorithm development. Instead, it focuses on the infrastructure and operations side of AI systems, including GPU-enabled environments, containerized deployment, Kubernetes orchestration, model serving, monitoring, and troubleshooting.

The ideal candidate has hands-on experience with AI infrastructure, cloud or data center operations, Docker, Kubernetes, GPU-based workloads, and production or production-like AI deployment environments. Teaching or mentoring experience is strongly preferred.

Key Responsibilities

  • Deliver instructor-led lectures, workshops, and technical walkthroughs
  • Lead hands-on labs related to Linux, Docker, Kubernetes, GPU infrastructure, and AI workload operations
  • Explain AI data center and infrastructure concepts in a beginner-friendly way
  • Support students with assignments, labs, troubleshooting, and capstone projects
  • Provide technical feedback and guidance to students
  • Help prepare or refine lab environments and practical exercises when needed
  • Work with the curriculum team to improve course content and student learning experience

Topics Covered
The instructor should be comfortable teaching or supporting topics such as:

  • AI data center fundamentals
  • AI infrastructure operations
  • GPU fundamentals and GPU-enabled workloads
  • Linux administration and troubleshooting
  • Python or shell scripting for automation
  • Docker and containerized deployment
  • Kubernetes fundamentals
  • Kubernetes pods, deployments, services, ingress, autoscaling, and Helm
  • Model serving and AI workload deployment
  • LLMOps and AIOps fundamentals
  • Monitoring and observability for AI workloads
  • Prometheus, Grafana, logging, and alerting concepts
  • Infrastructure troubleshooting
  • Cloud or data-center-like lab environments
  • Capstone project support

Required Qualifications

- 5+ years of experience in one or more of the following:

  • AI Infrastructure Operations
  • AI Data Center Operations
  • LLMOps / AIOps
  • MLOps
  • AI Platform Engineering
  • Platform Engineering
  • Site Reliability Engineering
  • DevOps Engineering
  • Cloud Engineering
  • Infrastructure Engineering

- Strong hands-on experience with Linux, Docker, and Kubernetes

- Experience deploying, operating, or supporting production or production-like systems

- Ability to explain technical concepts clearly to students with different levels of experience

- Strong troubleshooting and communication skills

Preferred Qualifications

  • Experience supporting AI, machine learning, or LLM workloads
  • Experience with GPU-enabled infrastructure or AI workload scheduling
  • Experience with NVIDIA GPUs, CUDA, GPU monitoring, or related AI infrastructure tools
  • Experience with model serving tools or platforms such as Triton Inference Server, vLLM, Ray Serve, MLflow, or Kubeflow
  • Experience with cloud platforms such as AWS, Azure, Google Cloud, or Alibaba Cloud
  • Experience teaching, mentoring, training, or delivering technical workshops
  • Kubernetes certifications such as CKA or CKAD are a strong plus
  • NVIDIA, AWS, Azure, or cloud infrastructure certifications are nice to have

Technical Skills

Strong experience with some or most of the following:

  • Linux
  • Docker
  • Kubernetes
  • Helm
  • Cloud infrastructure
  • GPU infrastructure
  • Monitoring and observability
  • Prometheus / Grafana
  • Logging and alerting systems
  • Model serving
  • LLMOps / AIOps
  • Infrastructure troubleshooting

Work Location

  • Remote/In-person to Saudi Arabia
  • We need both onsite and online instructors for this program.

Application question(s):

  • We need both onsite and online instructors, so would you be willing to travel to Saudi Arabia?

Work Location: Remote

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