$70-$200/hr DevOps and reliability work, on your schedule
Review AI-generated infrastructure, CI/CD, and reliability decisions the way you'd review a change before it touches prod. Flag the config that takes down the cluster, the rollback that won't work, the alert that's pure noise. The judgment earned running real systems under real load is what AI labs need.
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Hi, we're Zac and Jack, the founders of Terac. We want to talk to you directly, because you are the most important part of what we're building.
Terac is a community of experts. People who have spent years getting good at something specific and hard. The world is about to need more of you, not less. As AI takes on more of the world's work, the bottleneck shifts to the people who actually know what they're talking about.
Expert labor is the rarest resource in the world right now, and it is shockingly hard to find. The companies that need an SRE's eye on a deploy that will page someone at 3am spend weeks chasing people, paying placement fees, and settling for whoever is available. Meanwhile thousands of qualified people are sitting with knowledge that no one ever asks for.
That gap is what we're here to close. Every project that lands on Terac is routed to the people who actually know the answer, on their schedule, paid fairly, and only when the work is verified. No middleman taking a cut of your time. No vague gigs. No chasing checks.
We care about every single person in this community. If you join Terac, you're not a row in a database to us. We read the feedback. We answer the emails. We will fight for you when a customer is being unreasonable, and we will be honest with you when something on our side is broken. The quality of this panel is our entire company, and we owe you a serious bar.
If you've made it this far, here is what we're asking: claim your profile. Put your expertise on the record. Let the world's most ambitious teams come find you for the work only you can do.
DevOps & SRE questions
Still curious? Write to us at support@terac.com.
Narrow specializations are often the most valuable, because frontier models struggle most with the reasoning that lives at the edges of a field. If you can explain why you'd reach for eBPF over a sidecar proxy, or how a controller reconciliation loop should handle partial failures, that expert reasoning is exactly what model trainers need. Generalist SRE experience is also in demand, so you do not need to be a specialist to qualify.
Certifications like the CKA, CKS, or AWS DevOps Professional are useful signals of verified depth, and noting them during onboarding helps match you to the right tasks. However, they are not a hard requirement. Reviewers with strong production experience but no active cert are regularly accepted, and your practical knowledge carries more weight than a credential's expiration date.
No. Tasks are built around hypothetical systems, sanitized scenarios, and publicly documented architectures, not your employer's live infrastructure or proprietary configurations. You are being paid for your reasoning and judgment, not for access to systems you are obligated to protect. You should never submit anything that would violate your employment agreement or a vendor's terms of service.
The work varies by task but commonly includes reviewing AI-generated Terraform and Pulumi configurations for correctness and security posture, evaluating Kubernetes manifests and Helm charts against production-readiness standards, and critiquing CI/CD pipeline definitions written in tools like GitHub Actions or Argo Workflows. You may also be asked to write worked examples of incident response reasoning or SLO/SLI calculation to show a model how an experienced practitioner thinks through the problem.
Platform engineering experience is directly relevant and in active demand. Tasks frequently involve evaluating AI output on topics like internal Kubernetes platform design, golden-path templates, developer portal configuration, and service mesh policy, where classic SRE expertise alone leaves gaps. If your day-to-day centers on Backstage, Crossplane, or building paved roads for other engineers, that context is considered a distinct and valuable sub-domain.
Why your expertise matters
AI models increasingly generate Terraform, Helm charts, Kubernetes manifests, CI/CD pipeline configs, and incident runbooks - artifacts where a subtle misconfiguration can cascade into a production outage or a security breach. DevOps and SRE practitioners carry the judgment to catch the kind of mistakes a model would never flag on its own: a missing readinessProbe that causes thundering-herd restarts, a pod security policy that silently degrades to permissive, or a deploy pipeline that skips smoke tests under race conditions. Your domain expertise is the ground truth that teaches models the difference between code that compiles and infrastructure that actually holds under real load.
How pay works
Pay toward the top of the $70-$200/hr band reflects depth in high-stakes specializations: staff-level SRE experience with SLO-driven reliability programs, hands-on expertise in regulated environments (SOC 2, FedRAMP, PCI), or fluency across multiple cloud providers plus on-prem hybrid architectures. All work is remote and asynchronous - you pick up tasks when your schedule allows - and payment is released after your submitted work passes Terac's verification step, not on an hourly clock.
What the work looks like
A sample of the DevOps and reliability work you would pick up. Every project is scoped, remote, and paid on verified completion.
- Review an AI-generated Terraform module for a multi-region EKS cluster, flagging missing lifecycle rules, insecure IAM trust policies, and state-locking gaps that would cause real incidents.
- Evaluate a model-written incident runbook for a Redis memory saturation alert, identifying steps that assume manual access the on-call engineer may not have at 2 AM.
- Create a worked example of sizing and tuning a Prometheus recording rule for a high-cardinality service, annotating each decision so a model can learn the reasoning behind metric aggregation choices.
- Score a set of AI-generated GitHub Actions workflows on correctness, secret handling, and idempotency, providing line-level feedback in the style of a pull-request code review.
- Write a realistic postmortem for a hypothetical Kubernetes node-pressure eviction event, including a timeline, contributing factors, and a concrete action-items section that reflects real SRE practice.
- Assess whether an AI-proposed SLO definition for an internal API correctly captures user-visible reliability vs. internal health signals, and rewrite the error-budget burn-rate alert thresholds where they are wrong.
Specialties we match
DevOps & SRE projects span a wide range of focus areas. Tell us where you go deep and we route the work that fits.
- Kubernetes (EKS/GKE/AKS)
- Terraform / OpenTofu IaC
- Incident management and postmortems
- SLO/SLI/error budget design
- CI/CD pipelines (GitHub Actions, ArgoCD, Jenkins)
- Observability (Prometheus, Grafana, OpenTelemetry)
- Linux systems and kernel tuning
- Cloud networking and zero-trust (AWS VPC, GCP VPC, Cilium)
- Container security (Falco, OPA/Gatekeeper, Trivy)
- Chaos engineering (Chaos Monkey, Litmus, Gremlin)
- Database reliability (RDS, Spanner, etcd backup/restore)
- On-call runbook design and toil reduction
