Senior Technical Trainer — AI / DevOps

Accountabilities

• Design and develop technical courses

  • 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.

  • Create engaging instructional content

    • 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.

    • Develop and validate hands-on labs

      • 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.

      • Collaborate on quality and editorial development

        • 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.

        Requirements:

        • Hands-on DevOps and infrastructure experience

          • 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.

          • Current experience with AI technologies

            • 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.

            • Teaching and communication skills

              • 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.

              • Collaboration and adaptability

                • 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.

                • Additional qualifications considered an advantage

                  • 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.

                  Benefits:

                  • Competitive fixed fee per course, agreed upfront based on course length and topic complexity, with higher compensation for advanced topics and exceptional instructors.

                  • Paid freelance project that can be completed alongside an existing job, with no fixed weekly-hour requirement.

                  • A typical initial project duration of approximately three months, with a mutually agreed delivery plan.

                  • Potential to progress to a full-time role or take on additional course projects based on mutual fit.

                  • Fully remote working arrangements with flexible hours.

                  • Opportunity to collaborate with a global technical education team and reach a community of more than one million engineers.

                  • Dedicated production support for video editing, animation, lab infrastructure, and course publishing, allowing you to focus on teaching and technical content.

                  • Opportunity to share your expertise in AI, DevOps, cloud, and Kubernetes through practical, hands-on education.