AI DevOps Engineer

Hi there! We are South and our client is looking for a AI DevOps Engineer!

Note to Applicants:

• Eligibility: This position is open to candidates residing in Latin America.

• Application Language: Please submit your CV in English. Applications submitted in other languages will not be considered.

• Professional Presentation: We encourage you to showcase your professional experience by including a Loom video in the application form. While this is optional, candidates who provide a video presentation will be given priority.

• Interview Policy: The use of artificial intelligence (AI) tools during interviews are strictly prohibited. 

• Note: As part of the final stages of our hiring process, candidates may be asked to provide professional references for a reference check. If hired, you may also be asked to update your LinkedIn profile :)

 

Overview

Our Client is hiring AI DevOps Engineers to join its AI Operating Group (AI OG), a team focused on building and deploying AI-powered solutions for state and local government, education, and other public sector organizations.

This role bridges the gap between AI development and production infrastructure, ensuring that machine learning models, AI applications, and data pipelines are deployed securely, reliably, and at scale. The ideal candidate combines strong DevOps expertise with experience supporting AI and machine learning workloads in cloud-native environments.

Key Responsibilities

• Build, maintain, and optimize CI/CD pipelines for AI and machine learning deployments

• Deploy and manage containerized AI workloads using Docker and Kubernetes

• Monitor production environments, model performance, infrastructure health, and system reliability

• Collaborate with AI engineers, data scientists, and solution architects to streamline deployment processes

• Implement Infrastructure-as-Code (IaC) practices to improve scalability, consistency, and reproducibility

• Manage cloud infrastructure and platform services across AWS, Azure, and GCP environments

• Enforce security, compliance, and access control standards for AI systems

• Troubleshoot infrastructure and deployment issues while supporting incident response efforts

• Create and maintain operational documentation, deployment procedures, and technical runbooks

• Improve observability, monitoring, logging, and alerting frameworks for AI platforms

Required Qualifications

• 3+ years of experience in DevOps, MLOps, Platform Engineering, or related infrastructure roles

• Hands-on experience deploying and managing machine learning models in production environments

• Strong knowledge of containerization technologies and orchestration platforms

• Experience building and maintaining CI/CD pipelines

• Hands-on experience with Infrastructure-as-Code tools and cloud-native environments

• Familiarity with monitoring, logging, and observability solutions

• Strong understanding of security best practices for cloud and AI infrastructure

• Excellent written and verbal English communication skills

• Ability to work independently in a fully remote, U.S.-aligned environment

• Strong troubleshooting and problem-solving capabilities

Preferred Qualifications

• Experience working with government, education, or other regulated public sector organizations

• Familiarity with compliance frameworks such as FedRAMP, NIST, or similar regulatory standards

• Experience supporting LLM deployment pipelines, generative AI infrastructure, or AI platforms

• Experience with MLOps frameworks and model lifecycle management

• Cloud certifications (AWS, Azure, or GCP) are a plus

• Experience working in consulting or client-facing technical environments

Tech Stack

• Python

• Docker

• Kubernetes

• Terraform

• GitHub Actions

• AWS / Azure / GCP

• MLflow

• Apache Airflow

• Prometheus

• Grafana

• Ansible

 If this opportunity sounds good to you, send us your resume!