Cloud Infrastructure AI Trainer

About OpenTrain

OpenTrain is the #1 platform for building careers in AI training and data labeling. We help freelancers find and manage specialized AI training work, consolidate proof of experience, and grow a durable portfolio that employers and clients can trust.

As an OpenTrain contributor you join a fast-growing industry where human expertise directly shapes how AI systems behave. We support remote, flexible engagements across many technical domains.

About AI training work in this domain

AI training (data labeling / annotation) is the human side of building intelligent systems. For technical domains like cloud infrastructure, experts review materials, rate model responses, write realistic scenarios, and correct architectures so models learn accurate, production-ready behavior.

This role combines domain expertise with concise, high-quality feedback that improves AI understanding of cloud design, scalability, security, and operations.

The role

We are recruiting a Cloud Infrastructure AI Trainer to contribute expert-reviewed guidance, evaluations, and written scenarios that help train next-generation AI systems. This is contractor work for an experienced cloud professional who can interpret architecture artifacts, produce clear technical feedback, and author realistic infrastructure examples.

Work is remote and part-time, with a typical commitment of 20+ hours per week. The project uses document-based inputs and requires you to produce evaluation ratings and generated text (scenarios, explanations).

What you'll do

• Review cloud infrastructure designs and recommend improvements and best practices.
• Create and refine architecture documentation for robust, scalable, and secure environments.
• Use Kubernetes knowledge to improve container orchestration, deployment, and scaling strategies.
• Develop automation scripts that streamline cloud operations, integration, and monitoring.
• Contribute realistic cloud infrastructure scenarios and written examples to train models.
• Identify and flag security or compliance risks within provided cloud materials.
• Collaborate with interdisciplinary teams to keep submitted input technically accurate and relevant.

Requirements

You must be an experienced cloud practitioner with demonstrated, hands-on expertise and the ability to explain complex concepts clearly.

• Demonstrated expertise designing and managing cloud infrastructure on AWS, Azure, or GCP.
• Strong hands-on experience with Kubernetes and containerized environments.
• Proficiency in scripting (Python, Bash, or PowerShell) to produce or review automation.
• Deep understanding of cloud security, networking, and scalability concepts.
• Experience troubleshooting and optimizing cloud-based architectures.
• Proven ability to work independently and deliver high-quality input on schedule.
• Fluent English (project language).

Helpful background (preferred)

• Cloud certifications such as AWS Certified Solutions Architect, Google Professional Cloud Architect, or Azure Solutions Architect Expert.
• Experience with infrastructure-as-code tools like Terraform or CloudFormation.
• Familiarity with cloud migration strategies and hybrid cloud solutions.

Compensation & work setup

Contractor engagement, remote collaboration. Part-time work with a typical minimum of 20+ hours per week.

Hourly project work with a target pay range of USD 40 to 120 per hour. Exact rate depends on experience and deliverables.

How the work is evaluated and deliverables

This project uses document-based inputs. Your tasks will include evaluation ratings and text generation: rating model responses, writing explanatory text and realistic scenarios, and editing architecture documentation.

Labeling software is vendor-specified (OTHER). You will submit ratings, written scenarios, and technical feedback as structured deliverables according to project guidelines.

Who should apply

Apply if you are an expert cloud engineer, site reliability engineer, DevOps engineer, or architect with strong Kubernetes and scripting experience who enjoys teaching systems through clear, concise examples and technical feedback.

This role is ideal for professionals who want flexible, high-impact freelance work that helps shape how AI systems learn to reason about real-world cloud environments.