AI Infrastructure Engineer III

Mozn

AI Infrastructure Engineer III at Mozn building scalable AI infrastructure for AI models. Designing, operating, and automating AI platform capabilities in a collaborative team environment.

Posted 7/20/2026full-timeRemote • 🇪🇬 EgyptMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in AI Infrastructure and MLOps, with a strong focus on deploying and managing GPU clusters and AI/ML platforms. Proficient in automating infrastructure and optimizing performance for large-scale AI workloads.

Highest-signal resume keywords

AI Infrastructure EngineeringMLOps AutomationKubernetes ManagementGPU OptimizationCI/CD Pipeline Development

ATS Keywords

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Hard Skills

KubernetesKubeflowMLflowNVIDIA GPU TechnologiesPythonBashTerraformPrometheusGrafanaRay

Tools & Technologies

AWSGCPAzureHelmGitOpsAnsibleOpenTelemetryELK/OpenSearchTriton Inference ServerHugging Face

Certifications & Qualifications

Cloud CertificationsKubernetes CertificationsNVIDIA CertificationsAI/ML Certifications

Industry Keywords

AI PlatformsML PlatformsGPU ClustersDistributed TrainingModel ServingObservabilityInfrastructure as CodeData ScienceCloud NativeOpen-Source Contributions

Tech Stack

Tools & technologies

AnsibleAWSAzureCloudGoGoogle Cloud PlatformGrafanaKubernetesNode.jsPrometheusPythonPyTorchRayTensorflowTerraform

About the role

Key responsibilities & impact

AI Platform Engineering: Design, deploy, and operate enterprise AI/ML platforms.
Build self-service platforms for Data Scientists and ML Engineers.
Deploy and operate Kubeflow, MLflow, KServe, Ray, or similar AI platforms.
Design infrastructure supporting model training, experimentation, feature engineering, and inference.
Build highly available and scalable model serving infrastructure.
GPU Infrastructure: Design and operate GPU clusters for large-scale AI workloads.
Optimize GPU scheduling, utilization, sharing, autoscaling, and resource allocation.
Deploy and manage NVIDIA GPU Operator and GPU-enabled Kubernetes environments.
Optimize distributed GPU training performance across multi-node clusters.
Troubleshoot AI infrastructure performance bottlenecks.
MLOps & Platform Automation: Build CI/CD pipelines for ML workloads.
Automate AI infrastructure provisioning using Infrastructure as Code.
Implement monitoring and observability for GPU utilization, model serving, training jobs, and inference latency.
Collaborate closely with Data Science teams to improve platform usability, performance, and reliability.

Requirements

What you’ll need

4-6 years of experience in AI Infrastructure, MLOps, Platform Engineering, or Cloud Engineering.
Strong hands-on experience with Kubernetes.
Experience with Kubeflow, MLflow, or similar ML platform technologies.
Experience operating GPU infrastructure for AI workloads.
Strong understanding of NVIDIA GPU technologies, CUDA fundamentals, and GPU optimization.
Experience supporting distributed training workloads.
Experience with model serving platforms such as KServe, Triton Inference Server, Ray Serve, or similar.
Experience with AWS, GCP, OCI, or Azure AI platforms.
Experience automating infrastructure using Terraform, Helm, GitOps, or Ansible.
Strong scripting or programming skills in Python, Bash, or Go.
Experience with Prometheus, Grafana, OpenTelemetry, ELK/OpenSearch, or equivalent observability platforms.
Preferred Qualifications: Experience with PyTorch, TensorFlow, Hugging Face, or JAX.
Experience with distributed training frameworks such as Ray, DeepSpeed, Horovod, or NCCL.
Experience with Vector Databases, LLM infrastructure, RAG architectures, or GenAI platforms.
Experience operating inference platforms for large language models.
Experience supporting AI research or Data Science teams in production environments.
Contributions to Cloud Native, Kubernetes, AI, or ML open-source communities.
Cloud, Kubernetes, NVIDIA, or AI/ML certifications are a plus.

Benefits

Comp & perks

You will be at the forefront of an exciting time for the Middle East, joining a high-growth rocket-ship in an exciting space
You will be given a lot of responsibility and trust.
We believe that the best results come when the people responsible for a function are given the freedom to do what they think is best
The fundamentals will be taken care of: competitive compensation, top-tier health insurance, and an enabling culture so that you can focus on what you do best
You will enjoy a fun and dynamic workplace working alongside some of the greatest minds in AI
We believe strength lies in difference, embracing all for who they are and empowered to be the best version of themselves