Cloud DevOps Engineer

Cloud DevOps Engineer

The company 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. The company 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 the company, 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 the company's own infrastructure and some inside a client's — their repositories, their cloud account, their change process. The company 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 the company

The company 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 [link for members] or write to us at [link for members] .

Requirements

Basic qualifications

• 5+ years as a DevOps, SRE or systems engineer running production infrastructure, with the fundamentals that go with it: Linux administration, networking, Git, and scripting in Bash plus Python or Go.

• Kubernetes in production, at depth: not only deploying to a cluster someone else built, but provisioning, upgrading and debugging one when it misbehaves.

• Infrastructure as code as your working practice: Terraform or equivalent, with state, modules and environment parity you can defend.

• CI/CD pipelines you built and maintain — GitHub Actions, GitLab or equivalent — and container images you are responsible for.

• Cloud deployment experience on AWS, Azure or GCP, including the managed Kubernetes service and the cost model that comes with it.

• Monitoring and incident work: Datadog, CloudWatch or equivalent, and a track record of taking an incident from alert to root cause to a change that prevented the repeat.

• Security hardening of Linux, containers and Kubernetes as part of how you build, not as a separate phase.

• Working discipline around infrastructure cost and capacity. You can explain what a cluster costs to run and what you did about it.

• Active use of AI-assisted coding tools such as Claude Code, Cursor, or GitHub Copilot in real delivery work.

• Clear written and spoken English, C1 or above, and the confidence to explain a technical trade-off directly to a client.

• Bachelor's degree in Computer Science, a related field, or equivalent professional experience.

Preferred qualifications

• Service mesh and traffic management: Istio, Linkerd or equivalent, and the failure modes they introduce as well as the ones they solve.

• 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, or contributions to open-source infrastructure tooling and published technical writing.

Benefits

100% remote-first culture (work anywhere in Latin America)

Paid time off (PTO)

U.S. Holidays

Solid AI Training and certification

Mentored career development

Profit sharing

$US remuneration

Maternity coverage