DevOps Engineer – Apply As Soon As Possible

DevOps teams are increasingly expected to keep cloud infrastructure reliable while moving software from development to production faster. That makes practical experience with Kubernetes, infrastructure as code, CI/CD and Linux particularly valuable in roles that involve real production-style scenarios.

This contractor opportunity with micro1 is also connected to AI training work. Instead of requiring previous AI experience, the role uses your existing DevOps knowledge to create realistic technical tasks, develop reference solutions and evaluate AI-generated outputs. There are 30 openings, with compensation listed at $50–$100 per hour.

This is not passive income work; it requires consistent attention, communication, and follow-through.

Role Overview

Employer: micro1

Role: DevOps Engineer

Employment Type: Contractor

Location: Remote

Openings: 30

Compensation: $50–$100 per hour

In practical terms, this role is less about simply maintaining infrastructure and more about demonstrating how experienced DevOps engineers approach realistic engineering problems. You will work with production-style scenarios involving CI/CD, containers, orchestration, infrastructure as code, monitoring and incident response.

What matters most here is the ability to produce technically correct, reproducible work and explain why a particular solution works. Strong written and verbal communication is therefore just as important as knowing the tools.

Many people assume that an AI-related position requires previous AI engineering experience. In this role, the stated requirement is different: your DevOps domain knowledge is the foundation for helping train and evaluate next-generation AI systems.

Key Responsibilities

• Design realistic DevOps tasks based on production-style scenarios.
• Create scenarios involving CI/CD, containers, orchestration, infrastructure as code, monitoring and incident response.
• Write working reference solutions with clear setup and validation steps.
• Review AI-generated technical output and identify failures.
• Write precise corrections and explain the reasoning behind them.
• Maintain a high standard for correctness and reproducibility.
• Provide structured feedback that improves future task design.

You’ll do well here if you can look at an infrastructure problem, understand the underlying failure, build a practical solution and communicate the reasoning clearly. The position is particularly suited to engineers who are comfortable working independently and reviewing technical work critically.

Required Qualifications

• 3+ years of professional experience in DevOps, SRE, Platform or Infrastructure engineering.
• Strong coding ability in Python, Go, Bash or TypeScript.
• Practical experience with CI/CD tools such as GitHub Actions, GitLab CI or Jenkins.
• Hands-on experience with Docker and Kubernetes.
• Experience with at least one major cloud platform, including AWS, GCP or Azure.
• Infrastructure as Code experience with Terraform, Ansible, Pulumi or CloudFormation.
• Advanced Linux and Git skills.
• Professional written and spoken English.
• Availability of at least 20 hours per week.

Preferred Qualifications

• Experience with Prometheus, Grafana, Datadog or OpenTelemetry.
• Background in security, secrets management or platform reliability.
• Open source contributions or technical writing experience.
• Previous AI training data or model evaluation experience.

Who Should Apply?

This is not a role for someone who only has introductory exposure to DevOps tools. The stated requirements point toward an experienced engineer who can work confidently across infrastructure, automation, cloud environments and production-style troubleshooting.

It may be a strong fit if you have spent several years building or maintaining deployment pipelines, working with containers and Kubernetes, managing infrastructure through code, and troubleshooting Linux-based systems.

Previous AI training experience is preferred rather than required, so applicants should not assume that a lack of AI experience automatically excludes them.

How to Apply

Click Here to Apply

This opportunity is shared directly by the business owner. Communication is handled professionally, and applications are reviewed carefully. No fees are required at any stage of the process, and expectations are discussed clearly before any work begins.

This is a direct remote opportunity. Shortlisted candidates will receive clear next steps and role expectations before any engagement begins.

Sample ATS-Aligned CV

Curriculum Vitae

To download this CV as PDF, use your browser: Press Ctrl + P → Select “Save as PDF”

Interview Preparation

Role-Specific Questions

• How would you design a reliable CI/CD pipeline for a containerized application?
• What happens when a Kubernetes pod repeatedly enters a CrashLoopBackOff state, and how would you troubleshoot it?
• How would you structure Terraform code for infrastructure that needs to be maintained by multiple engineers?
• Describe how you would safely manage secrets in a cloud-based deployment environment.
• How would you investigate a production deployment that suddenly begins returning errors?
• What are the main differences between containers and virtual machines?
• How would you make a DevOps task reproducible for another engineer?
• How would you evaluate whether an AI-generated infrastructure solution is technically correct?
• What Linux commands and troubleshooting techniques do you commonly use when diagnosing an infrastructure problem?
• How would you design monitoring for a Kubernetes-based application?

General Interview Questions

• Tell us about your DevOps engineering experience.
• Why are you interested in this contractor opportunity?
• Describe a difficult infrastructure problem you solved.
• How do you prioritize tasks when working independently?
• How do you communicate technical problems to people with different levels of technical knowledge?
• How do you ensure your work is accurate before handing it over?
• How many hours per week are you available to commit?

Talking Points

• Prepare specific examples involving Kubernetes, Terraform, CI/CD, Docker and Linux.
• Explain not only what you changed but why you chose that approach.
• Be ready to demonstrate practical troubleshooting rather than only defining DevOps terminology.
• Emphasize reproducibility, documentation and validation.
• If you have no previous AI experience, focus on your ability to evaluate technical correctness and apply your existing DevOps expertise.

Interview Do’s and Don’ts

• Do explain your troubleshooting process step by step.
• Do provide concrete examples from professional projects.
• Do discuss trade-offs when describing technical decisions.
• Do demonstrate strong understanding of the tools listed in the vacancy.
• Don’t exaggerate your Kubernetes or cloud experience.
• Don’t rely entirely on memorized definitions.
• Don’t ignore security, reliability or reproducibility when discussing infrastructure.
• Don’t claim AI experience you do not have.

Preparation Checklist

• Review Kubernetes fundamentals and troubleshooting.
• Revise Terraform concepts and infrastructure-as-code practices.
• Review CI/CD workflows and common pipeline failures.
• Practice Docker troubleshooting.
• Refresh Linux administration and debugging commands.
• Review Git workflows.
• Prepare examples of cloud infrastructure projects.
• Practice explaining technical solutions clearly in professional English.

Recruiter insight: For a role like this, being able to explain why your solution is correct can matter as much as producing the solution itself. Prepare to demonstrate both technical depth and clear reasoning.