Cloud Engineering Instructor

About Edversity
Edversity is building outcome-driven technology education programs that help learners gain practical, job-relevant skills in emerging fields such as AI, Blockchain, Cybersecurity, Data, and Cloud. Our programs are designed around hands-on learning, real projects, mentorship, and employability. Our single measure of success is job placement: graduates getting hired into real cloud roles, not just credentialed ones.
We are now hiring a Cloud Engineering Instructor for our 12-week Cloud Engineering Program, focused on preparing complete beginners for entry-level Cloud Engineer, DevOps, Cloud Support, and Platform roles — locally and in the global remote/outsourcing market.

About the Role
We are looking for a hands-on cloud practitioner who has actually built and operated infrastructure in real environments and can teach students how cloud engineering works in the industry today — in the AI era, not 2020.
This is not a lecture-only role. The instructor will lead live sessions, run practical labs, review student work, mentor learners, and help them build a strong, GitHub-hostable portfolio of real deployed projects through hands-on, project-based learning.
The program is Google Cloud (GCP) primary, but the instructor must be genuinely fluent across AWS, Azure, and GCP, so they can teach concepts comparatively, prepare students for a market where all three appear, and adapt labs when needed.
The goal is simple: students should not just understand cloud concepts — they should be able to perform real cloud-engineer tasks with confidence, deploy working systems, and defend their architectural decisions in an interview.

Key Responsibilities

• Deliver live, hands-on cloud engineering sessions across a 12-week cohort of complete beginners
• Lead 3 sessions per week, 2 hours per session (6 live hours/week)
• Teach practical cloud engineering using real tools and real deployments — no slideware-only sessions
• Design and run labs covering cloud fundamentals, networking (VPC/firewalls), compute, storage, IAM, Infrastructure as Code (Terraform), containers (Docker, Cloud Run/GKE), CI/CD, monitoring and observability, and AI workloads on the cloud
• Weave AI-tooling fluency through the program — using AI copilots to write and debug infrastructure-as-code, AI-assisted troubleshooting and log analysis, and deploying AI/LLM workloads (e.g., Vertex AI) — while teaching students to verify AI output, not just trust it
• Review weekly lab submissions, IaC repos, deployed projects, quizzes, and written reports
• Provide clear, useful, constructive feedback on students' code, architecture, and documentation
• Guide learners in building portfolio-ready work: GitHub repositories with quality READMEs and architecture diagrams, live deployed applications, working CI/CD pipelines, and a substantial capstone project
• Support students with system-design fundamentals, mock technical interviews, explaining architecture to non-technical stakeholders, and practical career guidance for both local and remote/outsourcing job searches
• Guide certification readiness (e.g., Cloud Digital Leader / Associate Cloud Engineer, and AWS/Azure equivalents) — while treating certs as a floor, not the goal
• Keep the curriculum relevant as cloud services, AI tooling, and employer expectations evolve
• Set realistic, honest expectations with learners about cloud careers and what it actually takes to place — including the reality of the local vs remote market
  
  
  

What You Will Teach
The instructor should be comfortable teaching practical, hands-on topics across:

• Linux fundamentals, networking (IP, DNS, HTTP, subnets, routing, load balancing), Git/GitHub, and basic scripting
• Bash and Python for cloud automation
• Core cloud services: compute (VMs), object storage, virtual networking (VPC/VNet), and identity/access management (IAM) — taught GCP-first, with AWS and Azure equivalents
• Infrastructure as Code with Terraform (modules, remote state, multi-environment) — the baseline employer expectation
• Containers and orchestration: Docker, serverless containers (Cloud Run / equivalents), and Kubernetes fundamentals via a real GKE lab (honest depth, not faked)
• CI/CD pipelines with GitHub Actions (and cloud-native build/deploy), including keyless auth and deployment approvals
• Monitoring and observability: Cloud Monitoring/Logging, Prometheus, and Grafana, plus writing an incident post-mortem
• Deploying AI/LLM workloads on the cloud (e.g., Vertex AI / Gemini, with AWS Bedrock / Azure OpenAI awareness) and a data touchpoint (e.g., BigQuery)
• Using AI copilots (Gemini Code Assist, GitHub Copilot, Claude, Cursor) effectively — and verifying their output
• Cloud security and IAM fundamentals as a baseline, not a specialty
• Cross-cloud literacy across AWS, Azure, and GCP so students understand the wider market
• Interview preparation, cloud system-design fundamentals, documentation habits, and communicating trade-offs to non-technical stakeholders
  
  
  

What We Are Looking For
The ideal candidate should have:

• 3+ years of hands-on experience in a cloud engineering, DevOps, SRE, platform, or solutions-architecture role
• Genuine, expert-level practical command across AWS, Azure, and GCP — able to design, deploy, and troubleshoot on all three, with depth (not surface familiarity) in at least one
• Strong practical understanding of cloud networking, compute, storage, and IAM
• Real, production experience with Infrastructure as Code (Terraform), Docker and Kubernetes, and CI/CD pipelines
• Solid Linux skills and practical scripting ability in Bash and/or Python
• Experience deploying or operating AI/LLM or data workloads on the cloud (nice depth, but expected at least conceptually)
• Clear communication skills and the ability to explain complex topics in a simple, beginner-friendly way
• The ability to mentor students, review their code and architecture, and give practical, constructive feedback
• Fluency in English
  
  
  

Nice to Have

• Professional cloud certifications across providers — e.g., Google Professional Cloud Architect / Associate Cloud Engineer, AWS Solutions Architect / DevOps Engineer, Azure Administrator / Solutions Architect
• Experience with AI/ML infrastructure (Vertex AI, Bedrock, Azure OpenAI, SageMaker) or MLOps
• Exposure to FinOps / cloud cost optimization, and cloud security monitoring
• Familiarity with security/compliance frameworks relevant to cloud (e.g., NIST CSF, ISO 27001, SOC 2)
• Prior teaching, training, mentoring, or content-creation experience
• Freelance, remote, or client-facing cloud experience (Upwork, Toptal, direct international contracts)
• Public evidence of hands-on work: GitHub projects, live deployed applications, technical write-ups, or a blog
  
  
  

This Role Is Not For
This role is not suitable for candidates whose cloud experience is limited only to certificates, tutorials, or theoretical knowledge without real hands-on experience.
We are looking for someone who has actually provisioned infrastructure, written and shipped Terraform, deployed containers, built CI/CD pipelines, debugged production issues, and operated in a real cloud environment.

Who You Are

• A practitioner first — someone who has done the work, not just studied it
• Patient and student-focused, with a genuine interest in helping beginners grow into hire-ready engineers
• Clear, structured, and practical in your teaching style
• Reliable and consistent, because students depend on every session
• Honest about the cloud job market — including the realities of local vs remote placement — and what it takes to build a real career
• Comfortable teaching complete beginners while maintaining a strong practical standard
  
  
  

Program Details

• Format: Online
• Engagement type: Part-time / Contract
• Program duration: 12 weeks
• Schedule: Evenings, 3 sessions per week, 2 hours per session
• Weekly commitment: 6 live hours per week, plus preparation, grading, and student feedback
• Platform focus: Google Cloud (GCP) primary, with AWS and Azure taught comparatively
• Student level: Complete beginners
• Compensation: To be discussed, based on experience and engagement model
• Reporting line: Head of Programs