Infrastructure Analyst – AI, Part-Time

NCS

. Support and maintain the cloud and on-premises infrastructure required for AI workloads, ensuring the availability, performance, and security of model training and inference environments .

Posted 9/15/2026contractSão Paulo • BrazilMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in managing cloud and on-premises infrastructure for AI workloads, with a strong focus on performance, security, and automation. Proficient in container orchestration, CI/CD pipeline configuration, and monitoring of ML infrastructure.

Highest-signal resume keywords

Cloud Infrastructure ManagementContainer Orchestration (Docker, Kubernetes)CI/CD Pipeline ConfigurationInfrastructure as Code (Terraform, Ansible)Public Cloud Platform Experience (AWS, Azure, GCP)

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Cloud Infrastructure ManagementContainer ManagementCI/CD Pipeline ConfigurationInfrastructure as CodeMonitoring ToolsData Storage ManagementAI/ML Workload SupportSecurity ImplementationBackup and Disaster RecoveryLinux Administration

Tools & Technologies

DockerKubernetesTerraformAnsiblePrometheusGrafanaDatadogAWSAzureGCP

Certifications & Qualifications

AWS Solutions ArchitectAzure AdministratorGCP Associate

Industry Keywords

AI WorkloadsMLOpsModel TrainingModel InferenceHigh-Performance Data StorageObject StorageData LakesCloud GPUsSageMakerVertex AI

Tech Stack

Tools & technologies

AnsibleAWSAzureCloudDockerGoogle Cloud PlatformGrafanaKubernetesLinuxPrometheusTerraform

About the role

Key responsibilities & impact

Support and maintain the cloud and on-premises infrastructure required for AI workloads, ensuring the availability, performance, and security of model training and inference environments
Provision and manage cloud environments for AI workloads, including GPU instances and training clusters
Administer containers and orchestration platforms (Docker, Kubernetes) for model deployment
Configure and maintain CI/CD pipelines for AI applications
Monitor the performance, costs, and availability of ML infrastructure
Manage high-performance data storage, including object storage and data lakes
Implement security, backup, and disaster recovery policies
Automate provisioning using IaC (Terraform, CloudFormation, Ansible)
Support the engineering team with environment and deployment issues

Requirements

What you’ll need

Bachelor’s degree in Computer Science, Networking, Engineering, or a related field
3+ years of experience in IT infrastructure, including 1+ year working with AI/ML workloads
Experience with at least one public cloud platform (AWS, Azure, or GCP)
Knowledge of containers (Docker) and orchestration (Kubernetes)
Familiarity with Linux, networking, storage, and virtualization
Basic knowledge of MLOps and the AI model lifecycle
Cloud certifications (AWS Solutions Architect, Azure Administrator, GCP Associate) — preferred
Experience with cloud GPUs (SageMaker, Vertex AI, Azure ML) — preferred
Knowledge of Terraform and Ansible — preferred
Experience with monitoring tools (Prometheus, Grafana, Datadog) — preferred

Benefits

Comp & perks

Meal and food allowances (flexible card)
Transportation allowance (if required)
On-site parking
Health insurance with private-room coverage
Life insurance
Partnerships and discounts with educational and language institutions
Childcare assistance
Work on strategic Data & Analytics projects with a real impact on the business
Join a team that values collaboration and close communication throughout every delivery
Grow in an environment where friendship and professionalism go hand in hand