Systems Linux and DevOps Expert

Vraify; Deutschland; Remote; EUR 36,000 - 60,000; Full time

Benefits offered by this job

Remote project-based work

Job summary

Vraify is seeking engineers for a remote, project-based role focused on Linux, DevOps, and realistic task evaluation. You will build, solve, stress-test code, and explain failures with reproducible evidence across Linux, containers, Kubernetes, networking, observability, CI/CD, and production debugging.

You will test AI-generated code and agent traces for correctness, edge cases, regressions, unsafe assumptions, and incomplete fixes, while writing strong tests and concise explanations of failure

Qualifications

• Strong practical depth in Linux, shell workflows, containers and Kubernetes.
• Ability to communicate technical decisions in clear written English.
• Verifiable evidence such as GitHub work or a portfolio.

Responsibilities

• Create, solve, or review tasks involving Linux, shell workflows, containers, Kubernetes, networking, observability, CI/CD, infrastructure, and production debugging.
• Test AI-generated code and agent traces for correctness, edge cases, regressions, unsafe assumptions, and incomplete fixes.
• Write strong tests, reference patches, expected outcomes, and concise explanations of failure modes.
• Review peer-authored tasks for realism, reproducibility, difficulty, and evaluation criteria.

Skills

Linux

Shell workflows

Containers

Kubernetes

Networking

Observability

CI/CD

Infrastructure

Production debugging

Tools

Docker

Kubernetes

Git

Job description

Help us find where advanced coding agents break in systems linux and devops. Build or solve realistic tasks, stress-test generated code, and explain failures with reproducible evidence. Remote project-based work can pay up to $50/hour, depending on the project and demonstrated expertise.

What you'll do:

• Create, solve, or review realistic tasks involving Linux, shell workflows, containers, Kubernetes, networking, observability, CI/CD, infrastructure, and production debugging.


• Test AI-generated code and agent traces for correctness, edge cases, regressions, unsafe assumptions, and incomplete fixes.


• Write strong tests, reference patches, expected outcomes, and concise explanations of the failure mode.


• Review peer-authored tasks for realism, reproducibility, difficulty, and clean evaluation criteria.

What we look for:

• Strong practical depth in Linux, shell workflows, containers, Kubernetes, networking, observability, CI/CD, infrastructure, and production debugging.


• Ability to navigate unfamiliar code or specifications and communicate technical decisions in clear written English.


• Verifiable evidence such as meaningful GitHub work, shipped software, a competition profile, technical writing, or a portfolio. Confidential industry work can be described in the application without exposing proprietary material.


• Prior AI-evaluation experience is useful but not required; strong engineering judgement and testing discipline matter more.