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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