Review AI-generated solutions for cloud architecture and DevOps workflows. Evaluate code, configurations, and system designs for correctness, security, and best practices. Provide structured, actionable feedback to improve AI accuracy in engineering and IT domains.
Key Highlights
Key Responsibilities
Technical Skills Required
Benefits & Perks
Nice to Have
Job Description
Cloud & DevOps Testing Analyst (AI Training)
About The Role
AI is increasingly being used to generate infrastructure guidance, cloud architecture, and DevOps solutions — and it needs real engineers to keep it accurate. We're looking for experienced DevOps engineers, systems administrators, cloud architects, and software engineers to evaluate and improve AI-generated technical content.
Your hands-on expertise will directly shape how AI understands and communicates real-world infrastructure challenges. This is a fully remote, flexible contract role — work when you want, as much or as little as you like.
- Organization: Alignerr
- Type: Hourly Contract
- Location: Remote
- Commitment: 10–40 hours/week
- Review AI-generated solutions for cloud architecture, DevOps workflows, and IT infrastructure
- Evaluate code, configurations, and system designs for correctness, security, and best practices
- Identify errors, misconfigurations, and gaps in AI-generated infrastructure guidance
- Test and validate solutions across AWS, GCP, Azure, and related environments
- Provide structured, actionable feedback to improve AI accuracy in engineering and IT domains
- Assess troubleshooting recommendations for production systems and incident response scenarios
- Work independently and asynchronously — no meetings, no set hours
Interested in remote work opportunities in Devops? Discover Devops Remote Jobs featuring exclusive positions from top companies that offer flexible work arrangements.
- 3+ years of professional experience as a software engineer, systems administrator, DevOps engineer, or IT architect
- Hands-on experience with production systems and infrastructure troubleshooting
- Working knowledge of at least one major cloud platform (AWS, GCP, and/or Azure)
- Familiarity with CI/CD pipelines, containerization (Docker/Kubernetes), or infrastructure-as-code tools
- Strong analytical thinking and sharp attention to technical detail
- Clear written communication — you can explain complex concepts concisely
- No prior AI training experience required
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- Experience with monitoring and observability tools (Datadog, Prometheus, CloudWatch)
- Knowledge of networking, security, or compliance frameworks
- Background in system design or architecture reviews
- Scripting proficiency in Python, Bash, or similar languages
- Work on cutting-edge AI projects with top research labs
- Apply your real-world engineering expertise to shape how AI understands infrastructure
- Fully remote with complete schedule flexibility — work when it suits you
- Freelance autonomy with global collaboration
- Potential for ongoing work and contract extension
- Get hands-on exposure to advanced LLMs and how they're trained
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