Senior Technical Trainer — AI / DevOps

Jobgether; Brazil; Remote; BRL 67,000 - 112,000; Full time

Benefits offered by this job

Fully remote

Flexible hours

Competitive fixed fee per course

Potential path to full-time

Global engineering community

Job summary

Jobgether is seeking a Senior Technical Trainer for AI and DevOps focused course development from Brazil. This remote freelance role starts with a paid project of about three months and may lead to ongoing work or a full-time position.

You will create practical, hands-on labs and engaging video scripts while teaching engineers to apply AI agents, coding assistants, and AI tools to real infrastructure challenges.

Qualifications

• Hands-on DevOps and infrastructure experience.
• Current experience with AI technologies.
• Teaching and communication skills.
• Ability to work remotely with distributed teams.

Responsibilities

• Design and develop technical courses.
• Create engaging instructional content.
• Develop and validate hands-on labs.
• Collaborate on quality and editorial development.
• Record instructional videos and scripting.

Skills

DevOps hands-on

Linux

Kubernetes

CI/CD

IaC

AI tools

Teaching

Public speaking

Tools

Docker

Terraform

Git

Job description

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Technical Trainer — AI / DevOps based in Brazil.

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.

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.