DevOps Engineer
Posted 7 Oct 2026
Advertised as “Mid Azure DevOps Engineer”
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 2–4 yrs
- Management level
- Individual contributor
- Employment
- Remote
No salary listed for this role.
Mid Level DevOps Engineer (Azure)
Location: Latin America | 100% Remote
About CodeRoad
CodeRoad provides end-to-end software development services, helping businesses scale with ideal infrastructure solutions. From staff augmentation to dedicated IT teams and general software engineering, our nearshore technology services empower businesses to thrive in an ever-evolving digital landscape.
About the Role
CodeRoad is looking for an Intermediate DevOps Engineer to serve as the technical backbone for our Azure infrastructure, powering a cutting-edge AI and data platform delivered for a key client. You will own the environments hosting the platform's core data stores, AI services, and user-facing applications, collaborating closely with AI, data, and backend engineers to build a resilient and secure cloud ecosystem.
This role is critical to transforming our cloud operations into a foundation for LLM-based and agentic application architectures. By embedding security and best practices into the infrastructure from day one, you will directly drive the scalable and safe adoption of advanced AI capabilities for high-impact business applications.
Key Responsibilities
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Provision and manage end-to-end Azure infrastructure as code using Terraform or Bicep across development, staging, and production environments.
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Build and maintain robust CI/CD pipelines via Azure DevOps or GitHub Actions, while authoring comprehensive operational runbooks for the team.
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Optimize the data layer by provisioning, backing up, and securing relational databases, object storage, and vector databases with fine-grained access control.
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Lead security and secrets management by leveraging Azure Key Vault, enforce least-privilege Azure RBAC with managed identities, and establish secure API key lifecycles for LLM integration.
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Deploy and containerize backend Python services, custom web dashboards, and AI tooling for orchestration, observability, prompt evaluation, and agentic workflows.
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Collaborate with cross-functional PODs to enforce network isolation via private endpoints, virtual networks, and automated CI/CD security scanning, backed by Microsoft Defender for Cloud and .