Department: Academic / Training Programs Employment Type: Full-Time / Part-Time / Contract Location: Remote
About the Role
We are seeking an experienced instructor to teach a comprehensive DevOps, Cloud, Linux, and AI Infrastructure program. This role covers the full modern platform engineering stack — from Linux fundamentals through cloud architecture, automation, and AI infrastructure/MLOps — preparing students for high-demand roles in today’s technology landscape.
A DevOps and Cloud Engineering Instructor is responsible for teaching students how to build, deploy, automate, monitor, and manage applications and infrastructure in cloud environments, with an added focus on AI platform engineering and AI operations.
Core Subjects to Teach
Linux Systems Administration
- Linux installation and administration
- Shell scripting (Bash)
- User and permission management
- Process, memory, and storage management
- Systemd, logging, and troubleshooting
- Networking and security hardening
Cloud Engineering
- Cloud architecture and fundamentals
- Compute, storage, networking, and security
- Multi-cloud concepts using AWS, Microsoft Azure, and Google Cloud Platform (GCP)
- High availability and disaster recovery
DevOps Engineering
- Git and Git workflows
- CI/CD pipelines (Jenkins, GitLab)
- Infrastructure as Code with Terraform
- Configuration management with Ansible
- Containerization using Docker
- Orchestration using Kubernetes
- Monitoring with Prometheus and Grafana
AI Infrastructure & MLOps
- AI/ML infrastructure fundamentals
- GPU servers and acceleration
- AI workload deployment on Kubernetes
- Model serving and inference platforms
- Vector databases and AI data pipelines
- MLOps concepts and workflows
- Model monitoring and observability
- AI platform engineering
- Deployment of open-source LLMs
- Relevant tools: Kubeflow, MLflow, Ray, NVIDIA CUDA, NVIDIA AI Enterprise
What You Will Do
- Teach Linux administration and networking fundamentals.
- Explain DevOps culture, workflows, and best practices.
- Train students on version control using Git.
- Teach containerization with Docker and orchestration with Kubernetes.
- Train learners on Infrastructure as Code using Terraform and configuration management with Ansible.
- Teach CI/CD pipeline implementation with tools such as Jenkins or GitLab.
- Demonstrate cloud services on AWS, Microsoft Azure, or Google Cloud Platform.
- Teach monitoring and observability using Prometheus and Grafana.
- Guide students through AI infrastructure, MLOps workflows, and open-source LLM deployment.
- Guide students through hands-on projects and real-world deployments, culminating in a production AI platform project.
Requirements
Education & Experience
- Bachelor’s degree in Computer Science, Information Technology, or a related field.
- 5+ years of experience in Linux, Cloud, DevOps, Platform Engineering, or AI Infrastructure (3–5+ years minimum for DevOps/Cloud-focused tracks).
- Experience operating production Kubernetes environments.
- Experience with AI/ML deployments and GPU infrastructure.
- Hands-on cloud architecture experience.
- Prior training or teaching experience.
Technical Skills
- Linux system administration
- Networking (TCP/IP, DNS, HTTP, Load Balancing)
- Cloud computing across major providers
- Scripting (Bash, Python)
- CI/CD pipeline design and implementation
- Containers and Kubernetes
- Infrastructure as Code
- Monitoring and logging
- Security and DevSecOps
Soft Skills & Attributes
- Strong teaching and mentoring skills.
- Ability to translate complex technical concepts into clear, practical guidance for learners at varying levels.
- Organized and capable of managing coursework delivery across a multi-module, project-based curriculum.
- Passion for platform engineering, AI operations, and student success.
Preferred Qualifications
- Certifications such as AWS Certified Solutions Architect, AWS Certified DevOps Engineer, Certified Kubernetes Administrator (CKA), or HashiCorp Terraform Associate.
- Prior experience with curriculum development or instructional design.
Example Course Track (6–9 Months)
- Linux Administration
- Networking & Security
- Git & Automation
- Cloud Fundamentals
- Docker & Kubernetes
- Terraform & IaC
- CI/CD & DevSecOps
- Monitoring & Observability
- AI Infrastructure Fundamentals
- MLOps & LLM Deployment
- Production AI Platform Project
Career Outcomes
This combination of Linux, Cloud, DevOps, and AI Infrastructure is increasingly sought after for roles such as Platform Engineer, Cloud Architect, DevOps Engineer, Site Reliability Engineer (SRE), MLOps Engineer, and AI Infrastructure Engineer — making this instructor role a high-impact position for shaping in-demand career paths.