Cloud DevOps Engineer

Azumo

1d•Remote

About The Position

Cloud DevOps Engineer Azumo builds and operates production AI systems for companies ranging from seed-stage startups to Meta. We are hiring a Cloud DevOps Engineer to own the infrastructure those systems run on: the clusters, the pipelines that deploy to them, and the monitoring that says whether any of it is healthy. The role is fully remote across Latin America, aligned to your client's working day. You will not be handed a runbook. Azumo has shipped production infrastructure since 2016, and the work here is in systems that are already live: what breaks, what a cluster costs to run, and what nobody has automated yet because it was always easier to do by hand. Where this role sits At Azumo, ownership is split by what gets built: the pipelines, the method behind a decision, the product, and what an AI system does once it's live. This role owns what all of it runs on — clusters, deployment, and the infrastructure those systems depend on to stay up. A system that is down, a slow query nobody escalates, a cluster that costs more than it should: they are all yours. Recovery first, root cause after, and the change that keeps it from happening again. What you will build * Clusters that hold. Kubernetes in production — provisioning, upgrades, networking, and the workload configuration that decides whether a bad deploy takes one service down or all of them. * Infrastructure as code. Terraform or equivalent, with environments reproducible from the repository rather than from memory, and drift that gets detected instead of discovered. * Delivery pipelines. CI/CD that developers and QA can rely on, container images that are built the same way every time, and deployments to production that are unremarkable. * Observability and response. Monitoring and alerting that fires on what matters, plus the investigation, the root cause and the follow-up change when something does break. * Infrastructure for AI workloads. The clusters and inference services the AI Engineer lane deploys onto, whether that's hosted APIs or models we run ourselves, with the cost and scaling profile each one brings. * Cost and capacity. What the infrastructure costs to run, what it would cost at three times the traffic, and which of the two problems is worth solving now. * Hardening. Linux, Kubernetes, containers and the service mesh secured as a default rather than as a project, with secrets, access and audit trails that survive a client's review. * Tooling, documentation and support. The internal tooling your own team runs on, the documentation that makes any of it operable by someone else, and the developers and QA you unblock during the release cycle. Work inside the client's environment, when that's the engagement. Some of this work runs on Azumo's own infrastructure and some inside a client's — their repositories, their cloud account, their change process. Azumo is SOC 2 certified, client code stays in client repositories, and some engagements carry additional requirements such as HIPAA. How we work Our engineers build with AI every day. Claude Code, Codex, and similar tools are part of the standard toolchain here, not an experiment. We run an automated audit across the whole codebase on day one and every day after, grading security, cost, and architecture findings by severity with the exact file and line, so a small team can move quickly without quality drifting. We stay vendor-neutral across OpenAI, Anthropic, and open-weight models, and we run Valkyrie, our own production layer, when a single interface to any model is the right call. About Azumo Azumo is a San Francisco based software development company that has been building intelligent applications since 2016. We provide nearshore AI engineering teams to organizations that need production AI faster than they can hire for it: as an embedded engineering team, as AI staff augmentation alongside an existing team, or as a full project build. Our engineers work from Latin America, aligned to United States time zones, and have delivered for Twitter, Meta, Discovery Channel, Omnicom, UnitedHealth, and CENTEGIX. We hire for seniority and test for it before anyone joins a client team. We support engineers in going deep on the modern AI stack, and we give time back to open-source work, community teaching, and philanthropy. Apply at https://azumo.com/join-our-team or write to us at [email protected] .

Requirements

5+ years as a DevOps, SRE or systems engineer running production infrastructure.
Proficiency in Linux administration, networking, Git, and scripting in Bash plus Python or Go.
Experience with Kubernetes in production, including provisioning, upgrading, and debugging.
Experience with infrastructure as code: Terraform or equivalent, with state, modules, and environment parity.
Experience building and maintaining CI/CD pipelines (GitHub Actions, GitLab, or equivalent).
Experience with container images.
Cloud deployment experience on AWS, Azure or GCP, including managed Kubernetes services and cost models.
Experience with monitoring and incident management (Datadog, CloudWatch, or equivalent).
Track record of taking an incident from alert to root cause to a preventative change.
Experience with security hardening of Linux, containers, and Kubernetes.
Working discipline around infrastructure cost and capacity.
Active use of AI-assisted coding tools such as Claude Code, Cursor, or GitHub Copilot.
Clear written and spoken English, C1 or above.
Bachelor's degree in Computer Science, a related field, or equivalent professional experience.

Nice To Haves

Service mesh and traffic management: Istio, Linkerd or equivalent.
Experience with Helm, Kustomize or an equivalent approach to templating and environment configuration.
Database operations at production scale: PostgreSQL, MongoDB, RDS, DynamoDB or equivalent.
Running or scaling inference workloads, and the cost and latency trade-offs against hosted APIs.
Delivery under a compliance regime such as SOC 2 or HIPAA.
Cloud certifications.
Contributions to open-source infrastructure tooling.
Published technical writing.

Responsibilities

Own the infrastructure those systems run on: the clusters, the pipelines that deploy to them, and the monitoring that says whether any of it is healthy.
Build clusters that hold, including provisioning, upgrades, networking, and workload configuration.
Implement infrastructure as code using Terraform or equivalent.
Develop and maintain CI/CD pipelines for reliable delivery.
Implement monitoring and alerting, investigate issues, and perform root cause analysis.
Build and manage infrastructure for AI workloads, including clusters and inference services.
Manage infrastructure cost and capacity, explaining costs and proposing solutions.
Harden Linux, Kubernetes, containers, and service mesh.
Develop internal tooling, documentation, and provide support to unblock developers and QA.
Work inside the client's environment, including their repositories, cloud accounts, and change processes.