Platform Engineer (DevOps / Python)

About the job Platform Engineer (DevOps / Python)

Job Title: Platform Engineer (DevOps / Python)

Location: Remote – Latin America Preferred

Type of Contract: Full-Time | Remote | Contractor

Salary Range: Market Rates

Language Requirements: Professional English

We are seeking a skilled Platform Engineer (DevOps / Python) with strong expertise in cloud infrastructure and Kubernetes to join our growing team. You will play a key role in building and scaling an enterprise platform for AI development and orchestration. Your work will directly impact the speed and reliability of delivering production-ready AI systems for technical teams.

Key Responsibilities

Develop and maintain internal developer tools, CLI applications, and automation scripts using Python

Design, deploy, and manage Kubernetes environments across AWS, GCP, and Azure

Architect and implement secure cloud infrastructure, including IAM, networking, and access controls

Build infrastructure validation and testing frameworks to ensure reliability and scalability

Implement Infrastructure as Code (IaC) using Terraform and Helm

Develop and optimize CI/CD pipelines and GitOps workflows

Monitor, troubleshoot, and improve platform performance, observability, and cost-efficiency

Must-Have Qualifications

Strong professional experience with Python, including building CLI tools, SDKs, or API integrations

Deep hands‑on expertise with Kubernetes (RBAC, networking, custom resources)

Solid experience with AWS and/or GCP, especially IAM, networking, and workload identity (IRSA or equivalent)

Proven experience with Terraform and Helm in production environments

Strong understanding of CI/CD pipelines and infrastructure automation best practices

Knowledge of containerization, container registries, and security best practices

Solid Linux fundamentals and ability to troubleshoot complex infrastructure issues

Preferred Qualifications

Experience developing Kubernetes Operators or custom admission webhooks

Familiarity with observability tools such as Prometheus, Grafana, and Fluent Bit

Experience working with ML infrastructure or orchestration platforms (e.g., Flyte)

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