We are sharing a specialised part-time consulting opportunity for experienced DevOps, Site Reliability, and Cloud Engineering professionals with hands-on expertise in production infrastructure, cloud platforms, Kubernetes, CI/CD, observability, and infrastructure automation.
This sprint-based role supports an advanced AI research initiative focused on evaluating frontier coding models through realistic infrastructure engineering workflows. Selected professionals will use AI coding agents to complete technical tasks, review model-generated infrastructure implementations, identify reliability and engineering failures, and compare how different models perform across practical DevOps, SRE, and cloud scenarios.
Key Responsibilities
Infrastructure Engineering Evaluation
Review complex infrastructure engineering tasks completed with frontier AI coding agents
Evaluate implementations involving cloud platforms, Kubernetes, CI/CD systems, observability, and infrastructure automation
Assess technical correctness, reliability, maintainability, and operational readiness
Apply professional engineering judgment to realistic production infrastructure scenarios
AI Coding Agent Testing
Use frontier AI coding agents within practical infrastructure engineering workflows
Evaluate how effectively coding models interpret requirements and implement solutions
Identify bugs, edge cases, reliability issues, configuration errors, and failure modes
Assess where models require correction, additional prompting, or manual engineering intervention
Cloud & Platform Review
Evaluate solutions involving AWS, Azure, GCP, or comparable cloud environments
Review Kubernetes configurations, deployment workflows, and infrastructure orchestration
Assess Terraform or similar infrastructure-as-code implementations
Review CI/CD pipelines, monitoring, logging, alerting, and observability approaches
Identify security, scalability, resilience, and operational concerns where relevant
Model Comparison & Technical Judgment
Compare infrastructure solutions produced by multiple frontier coding models
Assess differences in implementation strategy, technical reasoning, reliability, and code quality
Determine which approaches best satisfy task requirements
Document model strengths, weaknesses, and recurring engineering failure patterns
Provide clear written assessments explaining relevant technical trade-offs
Ideal Profile
Strong candidates may have:
At least 2 years of professional DevOps, Site Reliability Engineering, or Cloud Engineering experience
Hands-on experience supporting production-scale infrastructure or distributed systems
Experience with AWS, Azure, GCP, Kubernetes, Terraform, CI/CD pipelines, or observability tooling
Regular use of AI coding agents within technical workflows
Strong ability to evaluate model-generated infrastructure and reliability engineering solutions
Experience diagnosing production issues, deployment failures, and infrastructure problems
Strong technical judgment, debugging skills, and written communication
Ability to work efficiently within short, intensive project sprints
Educational Background
A degree in computer science, software engineering, information technology, cloud computing, or a related technical discipline may be helpful
Advanced technical training in cloud infrastructure, systems engineering, networking, or DevOps may strengthen an application
Relevant cloud or infrastructure certifications may also be valuable
Equivalent professional experience supporting production systems may be considered
Nice to Have
Experience with AWS, Azure, or Google Cloud Platform
Strong Kubernetes and container orchestration experience
Expertise with Terraform or comparable infrastructure-as-code tooling
Background designing or maintaining CI/CD pipelines
Familiarity with observability platforms, monitoring, logging, and incident response
Experience with Cursor, Claude Code, Codex, Windsurf, Gemini CLI, or comparable AI coding tools
Knowledge of production reliability, scalability, disaster recovery, and performance engineering
Previous exposure to AI evaluation, benchmark development, or structured technical review
Why This Opportunity
Work directly with frontier AI coding agents on realistic infrastructure engineering problems
Apply DevOps, SRE, and cloud expertise to advanced AI evaluation
Identify subtle reliability and operational failure modes in model-generated solutions
Compare multiple coding systems across practical production workflows
Participate in intensive technical sprints with task-based compensation
Contract Details
Independent contractor role
Fully remote with flexible scheduling
Sprint-based project with task windows typically spanning approximately 12–24 hours
Compensation is $400 per accepted task
Typical tasks require approximately 2–3 hours after ramp-up
Compensation is tied to successfully accepted work
Work may include infrastructure implementation review, AI coding-agent evaluation, reliability analysis, debugging, and model comparison
Weekly payments via Stripe or Wise
Projects may be extended, shortened, or adjusted depending on scope and performance
Work will not involve access to confidential or proprietary information from any employer, client, or institution
About the Platform
This opportunity is available through 24-MAG LLC. We connect experienced professionals with remote consulting opportunities across technical, evaluation, and project-based workstreams.
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