Hire DevOps Engineers remotely from our vetted global talent

A DevOps engineer owns the platform layer that lets product engineers ship safely and quickly: the CI/CD pipelines that turn a merged pull request into a running production change, the infrastructure code that describes every cloud resource, the observability stack that explains what is actually happening in production, and the on-call practice that turns a 2 a.m. page into a fix rather than a fire drill. The role increasingly overlaps with platform engineering and SRE, and the distinction only matters when the team is big enough to staff them separately. At Terminal, DevOps hires are the engineers product teams reach for when deployment, infrastructure, or observability becomes the bottleneck on the roadmap.

CI/CD pipelines: The path from a green pull request to a running production change.

• GitHub Actions for most teams, GitLab CI where the source forge already lives there, Buildkite for fast self-hosted runners, Jenkins where the enterprise stack will not move

• Reusable workflows, composite actions, and the discipline to refuse a copy-pasted pipeline across services

• Build caching, parallel test sharding, and the runner topology that keeps pipeline time honest

• Deployment strategy picked by service: blue/green, canary, rolling, or progressive delivery with Argo Rollouts or Flagger

Infrastructure as Code: Every cloud resource described in code, reviewed in pull requests, and reproducible across environments.

• Terraform with remote state, workspaces, and module composition that actually scales past three services; OpenTofu where the team has moved off the license change

• Pulumi or AWS CDK when the team prefers a real programming language over HCL

• CloudFormation where AWS-native shops have already invested in stacks and StackSets

• Drift detection, plan review discipline, and the runbook for cleaning up state when reality and code disagree

Kubernetes and the GitOps layer: Container orchestration when the team has grown past a single PaaS.

• kubectl, Helm, and kustomize fluency, including knowing when raw manifests beat a Helm chart with twelve overrides

• ArgoCD or Flux for GitOps delivery, with sync waves, health checks, and the runbook for a failed sync

• Karpenter or Cluster Autoscaler for node provisioning, KEDA for workload-driven scaling

• An honest opinion on when not to use Kubernetes at all

Cloud platform fluency: Deep knowledge of at least one major cloud and working knowledge of the others.

• AWS, GCP, or Azure at depth, including IAM, VPC networking, managed databases, and serverless primitives

• Managed services chosen deliberately over self-hosted equivalents when the team cannot justify the operational cost

• Cost visibility built into the deploy path, not bolted on after the bill arrives

• Multi-account or multi-project boundaries that match the org chart, not the default tutorial

Observability and incident response: The work that makes production legible when the page fires.

• Datadog, the Grafana stack (Loki for logs, Tempo for traces, Mimir for metrics), Honeycomb, or New Relic chosen for the team's actual query patterns

• OpenTelemetry instrumentation as the default, not a vendor SDK per service

• PagerDuty or Opsgenie rotations with SLOs and SLIs that match what the customer actually feels

• Structured post-mortems that change behavior, not blameless theater that changes nothing

Common stacks worth knowing: Real-world DevOps engineers usually go deep in one or two combinations.

• AWS with Terraform, EKS, GitHub Actions, ArgoCD, and Datadog for product teams shipping at scale

• GCP with Terraform, GKE, Cloud Build or GitHub Actions, and the Grafana stack

• Azure with Bicep or Terraform, AKS, Azure DevOps or GitHub Actions, and Azure Monitor for enterprise teams

• Render, Fly, Railway, or Vercel as the deploy target when the team is small enough that Kubernetes is the wrong answer

• HashiCorp stack (Terraform, Vault, Consul, Nomad) where the team has already invested in it

The case for a DevOps hire is almost always a leverage argument. When product teams are blocked by deployment, infrastructure, or observability gaps, the cost of those gaps compounds across every engineer on the team. Hiring a DevOps engineer who owns the platform full-time turns that compounding cost back into compounding velocity. The case against shows up when the team is still small enough that a managed platform absorbs the problem.

Product teams are blocked at the platform layer: When the bottleneck is not the application code.

• Deploys that take longer than the change being deployed, with no clear owner of the pipeline

• Incidents diagnosed by guessing because the observability stack does not answer the question

• Cloud bills growing faster than usage, with no one tracking the cost drivers per service

• Security findings, dependency updates, and compliance work piling up because no one owns the platform

The team is large enough to need a platform owner: Roughly the eight-engineer threshold, give or take the product.

• Eight or more engineers shipping into shared infrastructure that no one fully owns

• Multiple services with diverging deploy paths, monitoring conventions, and on-call habits

• An on-call rotation that pages product engineers for infrastructure problems they cannot fix

• A backlog of platform work that gets deprioritized every sprint in favor of features

Reliability and compliance are non-negotiable: Anywhere downtime or audit failure has a real dollar cost.

• SLOs and error budgets tracked as product KPIs, not aspirational dashboards

• SOC 2, ISO 27001, HIPAA, or PCI evidence collected from the infrastructure rather than reconstructed at audit time

• Supply chain security with Sigstore, SLSA attestations, and signed container images verified at deploy

• Secrets management through Vault, AWS Secrets Manager, or GCP Secret Manager, not environment variables in a wiki

AI Fluency multiplier: Agentic AI workflows have changed how DevOps engineers ship platform work, and the gains compound on infrastructure-as-code.

• An AI Enabled engineer running Cursor or Claude Code with human-in-the-loop review can refactor Terraform modules, generate matching tests, and update the runbook in a single session

• An AI Native engineer orchestrates parallel agents for incident triage, correlating logs, traces, and recent deploys before a human reads the page

• Automated runbook generation and observability-correlated bug investigation move from aspirational to default once agents are part of the workflow

• Terminal classifies every engineer in AI Assisted, AI Enabled, or AI Native tiers and surfaces those signals at hire time

When not to hire a DevOps specialist: Managed platforms win on small teams.

• Pre-product-market-fit startups where AWS is too much overhead compared to Render, Fly, or Vercel

• Product teams of two to five engineers where a full stack engineer can own the deploy path with a managed PaaS underneath

• Prototypes where the infrastructure will be thrown away once the product direction settles

• Hire a senior full stack or backend engineer with strong operational instincts when the work is not yet platform-shaped

A senior DevOps engineer's job description is broader than the job posting suggests, but the day-to-day is concrete. Here is what they actually own.

CI/CD ownership end to end: The default unit of work.

• Design the pipeline, write the workflow, ship the runner topology, monitor the pipeline time as a product metric

• Reusable workflows and composite actions that the rest of the team can compose without reading the source

• Build caching, test sharding, and the discipline to refuse a pipeline that takes longer than the change

• Pair with product engineers on the deploy path so they can ship without filing a ticket

Infrastructure as Code at depth: Every cloud resource described in code, reviewed in pull requests, and reproducible across environments.

• Terraform or OpenTofu module design that scales past three services without copy-paste

• Pulumi or CDK where the team has chosen a real programming language for infrastructure

• Remote state, workspace strategy, and the runbook for cleaning up drift before it causes an incident

• Plan review discipline that catches a destructive change before apply, not after

Kubernetes and platform delivery: When the team has outgrown a single PaaS, the cluster becomes the platform.

• Helm charts and kustomize overlays that match the team's actual environments, not a default tutorial

• ArgoCD or Flux for GitOps delivery, with sync waves, health checks, and rollback runbooks

• Karpenter, Cluster Autoscaler, or KEDA configured to match the workload, not the demo

• Knowing when to skip Kubernetes entirely and ship to a managed runtime instead

Observability and on-call: The senior bar is making production legible before the page fires.

• Instrument every service with OpenTelemetry traces, structured logs, and metrics that match the SLOs

• Datadog, the Grafana stack, Honeycomb, or New Relic configured for the team's actual query patterns

• PagerDuty or Opsgenie rotations the engineer is actually on, not just the one they configured for product teams

• Post-incident reviews that change the system or the process, not just the wiki

Security and supply chain: The work that keeps the auditors and the attackers out.

• Secrets management through Vault, AWS Secrets Manager, GCP Secret Manager, or Doppler with rotation actually rehearsed

• SAST, DAST, and SCA integration in the pipeline with findings triaged like real bugs, not aspirational tickets

• Container image scanning with Trivy, Snyk, or Grype, blocking on critical findings rather than warning on them

• Supply chain attestations with Sigstore, SLSA, and signed images verified at admission

Platform engineering and golden paths: Reducing the cognitive load for product teams is the point of the role.

• Internal developer platforms with Backstage, Port, or a custom portal where the team has the headcount for one

• Golden paths for the most common workflows: spin up a new service, add a database, expose an endpoint, ship a job

• Self-service tooling that does not require a Slack message to use, with documentation that survives the engineer who wrote it

• Templates and scaffolding that match the team's standards rather than the framework defaults

Cross-team collaboration: A lot of the work happens outside the editor.

• Partner with product engineers on the deploy path so they can ship without escalating to the platform team

• Partner with security on the controls that have to be in the pipeline versus the ones that live elsewhere

• Partner with finance on cloud cost attribution so the bill maps back to a product owner

• Mentor junior engineers through code review, pair programming, and on-call shadowing

The skill bar separating a senior DevOps engineer from a generalist is depth in a few areas, not breadth across all of them. Terminal screens for both. Only the top 7% pass our screening, and the skills below are the ones that come up in technical interviews.

CI/CD fluency: Production experience designing the pipeline, not just maintaining one someone else built.

• GitHub Actions, GitLab CI, Buildkite, or Jenkins at depth, including reusable workflows and composite actions

• Deployment strategy chosen by service: blue/green, canary, rolling, or progressive delivery with Argo Rollouts or Flagger

• Build caching, test sharding, and runner topology tuned to keep pipeline time honest

• An opinion on when self-hosted runners earn their operational cost

Infrastructure as Code at depth: Real production experience, not a tutorial walkthrough.

• Terraform or OpenTofu at depth, including module composition, remote state, and workspace strategy

• Pulumi or CDK familiarity where the team has chosen a real programming language for infrastructure

• CloudFormation experience for AWS-native shops with existing stacks and StackSets

• Comfort reading a plan, refusing a destructive change, and resolving drift without a Slack thread

Kubernetes literacy and judgment: Production experience operating clusters, plus the judgment to know when to skip them.

• kubectl, Helm, and kustomize fluency, including the failure modes of each

• ArgoCD or Flux for GitOps delivery, with sync waves, health checks, and rollback runbooks

• Karpenter or Cluster Autoscaler for node provisioning, KEDA for workload-driven scaling

• The senior tell is knowing when not to use Kubernetes at all

Cloud platform depth: Real production experience with at least one major cloud.

• AWS, GCP, or Azure at depth, including IAM, VPC networking, managed databases, and serverless primitives

• Multi-account or multi-project boundaries designed to match the org chart, not the default tutorial

• Cost visibility tooling integrated into the deploy path with attribution back to a product owner

• An honest opinion on managed services versus self-hosted equivalents and the trade-offs each implies

Containerization and image security: Containers as a production tool, not just a local development convenience.

• Docker fluency including multi-stage builds, layer caching, and distroless or minimal base images

• buildx for cross-platform builds and BuildKit features the team actually uses

• Image scanning with Trivy, Snyk, or Grype blocking on critical findings in the pipeline

• Supply chain attestations with Sigstore, SLSA, and signed images verified at admission

Observability at depth: Knowing what to measure is as important as knowing how to collect it.

• OpenTelemetry as the default instrumentation, not a vendor SDK per service

• Datadog, the Grafana stack (Loki, Tempo, Mimir), Honeycomb, or New Relic configured for the team's query patterns

• SLOs, SLIs, and error budgets tied to user-visible behavior, not just system metrics

• Distributed tracing fluency, including the discipline to refuse a trace fan-out that costs more than it answers

Security and incident response: The non-optional parts of the role.

• Secrets management through Vault, AWS Secrets Manager, GCP Secret Manager, or Doppler with rotation actually rehearsed

• SAST, DAST, and SCA integration in the pipeline with findings triaged like real bugs

• PagerDuty or Opsgenie rotations the engineer has actually been on, with a story about an on-call practice they changed after a bad night

• Structured post-mortems that change the system, not just the wiki

AI Fluency: The capability shift that is reshaping engineering output.

• Daily use of Claude Code, Cursor, GitHub Copilot, or comparable AI coding assistants for infrastructure-as-code, pipeline, and runbook work

• Agentic IaC refactors that span Terraform modules, tests, and documentation in a single session, with human-in-the-loop review

• AI-driven incident triage that correlates logs, traces, and recent deploys before a human reads the page, plus automated runbook generation for the resulting fix

• AI Enabled or AI Native tier per Terminal's standard. The engineer either uses AI tools to compound their output significantly, or builds agentic workflows directly

Soft skills that matter: The non-technical bar is real.

• Clear written communication. Most platform work happens in pull requests, design docs, runbooks, and async threads

• Pragmatism on scope. Knowing when to ship the imperfect platform and when to refuse the request that does not scale

• Mentorship instinct. Senior engineers raise the floor of the whole team

• Calm under production pressure. The failed deploy, the noisy page, the cost spike that shows up on a Friday afternoon