Remote Director Cloud Infrastructure Engineering Jobs in Ridgewood, NJ

Specialised Part-Time Consulting OpportunityWe 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 ResponsibilitiesInfrastructure Engineering EvaluationReview complex infrastructure engineering tasks completed with frontier AI coding agentsEvaluate implementations involving cloud platforms, Kubernetes, CI/CD systems, observability, and infrastructure automationAssess technical correctness, reliability, maintainability, and operational readinessApply professional engineering judgment to realistic production infrastructure scenariosAI Coding Agent TestingUse frontier AI coding agents within practical infrastructure engineering workflowsEvaluate how effectively coding models interpret requirements and implement solutionsIdentify bugs, edge cases, reliability issues, configuration errors, and failure modesAssess where models require correction, additional prompting, or manual engineering interventionCloud & Platform ReviewEvaluate solutions involving AWS, Azure, GCP, or comparable cloud environmentsReview Kubernetes configurations, deployment workflows, and infrastructure orchestrationAssess Terraform or similar infrastructure-as-code implementationsReview CI/CD pipelines, monitoring, logging, alerting, and observability approachesIdentify security, scalability, resilience, and operational concerns where relevantModel Comparison & Technical JudgmentCompare infrastructure solutions produced by multiple frontier coding modelsAssess differences in implementation strategy, technical reasoning, reliability, and code qualityDetermine which approaches best satisfy task requirementsDocument model strengths, weaknesses, and recurring engineering failure patternsProvide clear written assessments explaining relevant technical trade-offsIdeal ProfileStrong candidates may have: At least 2 years of professional DevOps, Site Reliability Engineering, or Cloud Engineering experienceHands-on experience supporting production-scale infrastructure or distributed systemsExperience with AWS, Azure, GCP, Kubernetes, Terraform, CI/CD pipelines, or observability toolingRegular use of AI coding agents within technical workflowsStrong ability to evaluate model-generated infrastructure and reliability engineering solutionsExperience diagnosing production issues, deployment failures, and infrastructure problemsStrong technical judgment, debugging skills, and written communicationAbility to work efficiently within short, intensive project sprintsEducational BackgroundA degree in computer science, software engineering, information technology, cloud computing, or a related technical discipline may be helpfulAdvanced technical training in cloud infrastructure, systems engineering, networking, or DevOps may strengthen an applicationRelevant cloud or infrastructure certifications may also be valuableEquivalent professional experience supporting production systems may be consideredNice to HaveExperience with AWS, Azure, or Google Cloud PlatformStrong Kubernetes and container orchestration experienceExpertise with Terraform or comparable infrastructure-as-code toolingBackground designing or maintaining CI/CD pipelinesFamiliarity with observability platforms, monitoring, logging, and incident responseExperience with Cursor, Claude Code, Codex, Windsurf, Gemini CLI, or comparable AI coding toolsKnowledge of production reliability, scalability, disaster recovery, and performance engineeringPrevious exposure to AI evaluation, benchmark development, or structured technical reviewWhy This OpportunityWork directly with frontier AI coding agents on realistic infrastructure engineering problemsApply DevOps, SRE, and cloud expertise to advanced AI evaluationIdentify subtle reliability and operational failure modes in model-generated solutionsCompare multiple coding systems across practical production workflowsParticipate in intensive technical sprints with task-based compensationContract DetailsIndependent contractor roleFully remote with flexible schedulingSprint-based project with task windows typically spanning approximately 12–24 hoursCompensation is $400 per accepted taskTypical tasks require approximately 2–3 hours after ramp-upCompensation is tied to successfully accepted workWork may include infrastructure implementation review, AI coding-agent evaluation, reliability analysis, debugging, and model comparisonWeekly payments via Stripe or WiseProjects may be extended, shortened, or adjusted depending on scope and performanceWork will not involve access to confidential or proprietary information from any employer, client, or institutionAbout the PlatformThis 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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