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United States
Remote only
Remote, worldwide · Freelance course project with a path to full-time
We’re hiring on behalf of a global hands-on tech-education platform used by over a million engineers to learn DevOps, cloud, Kubernetes and AI. Its courses are built around real labs, not slides, and taught by practising engineers who are great on camera.
We’re looking for a DevOps engineer who loves teaching to create and present a course on AI for DevOps: how engineers use AI agents and modern AI tooling in real infrastructure work.
This role suits you if you already teach as part of what you do: you’ve published courses on Udemy, Pluralsight or YouTube, written a book, worked in DevRel, run workshops, or regularly speak at meetups and conferences.
How it works
It starts with one paid course project. You can do it alongside your current job. There’s no fixed weekly-hour requirement, just a delivery plan you agree on together. A typical project takes around 3 months.
The goal is long-term. The team is growing its internal trainer pool, and the first project is how both sides find out if it’s a fit. After that, it can lead to a full-time role or more course projects.
A fixed fee per course is agreed upfront, based on course length and topic complexity. Fees are competitive, and advanced topics and exceptional instructors are paid accordingly. Payment is made on delivery of the completed course.
Fully remote with flexible hours. Expect a weekly sync, Slack communication, and some time-zone overlap with a Europe/Asia-based team.
What you’ll do
Shape the course with the curriculum team: sections, lessons, and hands-on labs.
Write lab specs that a dedicated labs team builds, then test them.
Write and narrate short, story-driven video lessons (about 6 minutes each). You’re welcome to use AI to draft, but the final script should sound like you.
Record demo and lab-solution walkthroughs.
Work through structured editorial feedback, especially on your first section.
Video editing, animation, lab infrastructure and publishing are all handled by the production team. You focus on the teaching.
What we’re looking for
Hands-on DevOps foundations: Linux, containers, Kubernetes, a major cloud, CI/CD, IaC. You build and run infrastructure yourself.
Real, current use of AI in your engineering work: AI agents, coding assistants, LLM-based automation. Deep AI specialisation isn’t required, but curiosity and staying current are.
Excellent spoken and written English. All courses are scripted, narrated and delivered in English, so you need clear, natural, easily understood English and confidence on camera.
A genuine passion for teaching, shown through courses, tutorials, talks, workshops or mentoring you’ve done because you enjoy it.
The ability to explain complex topics simply, with empathy for beginners.
Clear, engaging spoken English and confidence on camera.
Openness to an established teaching methodology and to editorial feedback.
Nice to have
Published online courses or a book.
Conference talks, a growing YouTube channel or blog, or community roles (CNCF Ambassador, AWS Community Builder, Microsoft MVP, etc.).
Kubernetes (CKA/CKAD/CKS, Kubestronaut) or AWS/Azure certifications.
How to apply
Send your CV or LinkedIn profile, plus a link that shows how you teach: a course, a talk, a tutorial or a short video.
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