Senior Technical Trainer — AI / DevOps

As a Senior Technical Trainer, you will combine hands-on DevOps expertise with a passion for teaching to develop practical, engaging courses on AI-powered engineering workflows. You will help engineers understand how to apply AI agents, coding assistants, and modern AI tools to real-world infrastructure and automation challenges. Working alongside a curriculum team and experienced technical educators, you will design lessons, develop hands-on lab specifications, and record clear, compelling instructional videos. This remote freelance opportunity begins with a paid course project, typically lasting around three months, with the potential for ongoing projects or a full-time position. You will have the flexibility to work alongside existing commitments while contributing your technical knowledge to a global engineering community. The ideal candidate is a practising DevOps engineer who can simplify complex concepts, communicate confidently on camera, and create learning experiences that make advanced technologies accessible.

• Collaborate with the curriculum team to define course structure, learning objectives, lesson sequences, and practical lab exercises focused on AI for DevOps.

• Develop content that demonstrates how engineers use AI agents, coding assistants, and LLM-based automation in real infrastructure environments.

• Ensure lessons are technically accurate, relevant, practical, and aligned with the needs of working engineers.

• Write concise, story-driven video scripts designed to explain technical concepts clearly, with individual lessons typically lasting around six minutes.

• Use AI tools where helpful during drafting while ensuring the final scripts reflect your own voice, expertise, and teaching style.

• Narrate and present lessons confidently in English, making complex technical topics approachable for learners at different experience levels.

• Write detailed specifications for practical labs that demonstrate real-world DevOps and AI workflows.

• Collaborate with the dedicated labs team responsible for building the lab environments.

• Test completed labs, validate technical accuracy, and ensure exercises deliver the intended learning outcomes.

• Record demonstrations and walkthroughs explaining how to complete lab exercises and solve practical challenges.

• Work closely with curriculum and production teams throughout the course development process.

• Incorporate structured editorial feedback, particularly during the initial course section, to align content with established teaching standards.

• Participate in regular team syncs and maintain communication through collaboration tools such as Slack.

• Focus on technical expertise, teaching, scripting, and recording while the production team manages video editing, animation, lab infrastructure, and publishing.

• • Previously published online courses on platforms such as Udemy or Pluralsight, or experience authoring a technical book.

  • Experience speaking at conferences or meetups, maintaining a technical YouTube channel or blog, or contributing to technical communities.

  • Community credentials or roles such as CNCF Ambassador, AWS Community Builder, or Microsoft MVP.

  • Relevant Kubernetes certifications, including CKA, CKAD, or CKS, or recognition such as Kubestronaut.

  • Cloud certifications from providers such as AWS or Microsoft Azure.

• Strong practical knowledge of Linux, containers, Kubernetes, at least one major cloud platform, CI/CD, and infrastructure as code (IaC).

• Experience building, operating, and maintaining infrastructure rather than relying solely on theoretical knowledge.

• The ability to demonstrate real engineering workflows and translate practical experience into effective learning materials.

• Practical experience using AI tools in engineering work, including AI agents, coding assistants, and LLM-based automation.

• Genuine curiosity about emerging AI capabilities and a commitment to staying current with new tools and techniques.

• The ability to explain how AI can support infrastructure engineering and DevOps workflows; deep specialization in AI research is not required.

• A demonstrated passion for teaching, supported by experience creating courses, publishing tutorials, delivering talks or workshops, mentoring colleagues, or sharing technical knowledge with a community.

• Excellent written and spoken English, with the ability to communicate clearly, naturally, and confidently on camera.

• Strong storytelling skills and the ability to break down complex technical concepts into simple, structured explanations.

• Empathy for learners, particularly beginners, and an understanding of how to make technical material engaging and accessible.

• Willingness to follow an established teaching methodology and incorporate constructive editorial feedback.

• Ability to work independently, organize deliverables, and meet an agreed project schedule.

• Comfort working remotely with a distributed team, including participation in weekly syncs and some time-zone overlap with colleagues based in Europe and Asia.