Mid Azure DevOps Engineer

Role overview A mid-level cloud engineering position focused on building and operating Azure infrastructure that supports an AI and data platform. The role spans environment provisioning, CI/CD automation, data-layer security, and collaboration with AI, data, and backend engineers across distributed teams. It is a fully remote opportunity based in Latin America.

Responsibilities - Provision and manage Azure environments across development, staging, and production using infrastructure-as-code tooling. - Build, maintain, and document CI/CD pipelines and operational runbooks that enable reliable deployment workflows. - Provision, secure, and back up relational databases, object storage, and vector databases with fine-grained access control. - Manage secrets, identities, and role-based access policies, including secure key lifecycles for AI service integrations. - Containerize and deploy backend Python services, internal dashboards, and AI tooling for orchestration, observability, and evaluation. - Partner with cross-functional teams to enforce network isolation, automated security scanning, and cloud monitoring across the platform.

Requirements - Two to four years of DevOps or cloud engineering experience, including at least two years working specifically with Azure. - Hands-on experience with Terraform or Bicep, container hosting platforms, and scripting in Python or Bash. - Practical knowledge of Azure SQL, Blob Storage, Azure Key Vault, and CI/CD systems such as Azure DevOps or GitHub Actions. - Familiarity with network isolation patterns, encryption at rest and in transit, and vulnerability or policy scanning in pipelines. - Strong sense of ownership, proactive problem-solving ability, and comfort working in fast-paced, collaborative settings. - Advanced English proficiency for clear written and spoken communication with distributed teams.

Nice to have - Experience with vector databases and hosting Python services in the cloud. - Familiarity with LLM and agentic application tooling such as LangGraph, Langfuse, Promptfoo, or MCP server deployments. - Exposure to modern data integration platforms including Microsoft Fabric, Azure Data Factory, or Databricks. - Background configuring AI-specific security controls, prompt logging, and token usage telemetry.

Benefits and work setup - Fully remote work arrangement. - Paid holidays and generous paid time off. - Health insurance assistance. - Competitive compensation denominated in USD. - Continuous learning and career growth opportunities.