AI Infrastructure Engineer III

🕒 August 4

🇪🇬 Egypt – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

👷 Infrastructure Engineer

👻 Ghost score 15%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Mozn

WebsiteLinkedInAll Job Openings

201 - 500 employees

Founded 2017

💼 Consulting

🏥 Healthcare

📦 Logistics

💰 $10M Series A on 2022-02

Consulting • Healthcare • Logistics

Mozn is a regional AI company building Arabic-native generative AI and enterprise AI platforms. It provides OSOS (an Arabic-first GenAI platform), FOCAL (a financial-crime and fraud detection platform), and customized AI solutions spanning language intelligence, risk intelligence, operational AI, data management, geospatial intelligence, and AI centers. Mozn focuses on serving enterprise customers in the MENA region (including healthcare, finance, and government) with SaaS products and tailored AI services that prioritize cultural relevance, data security, and regulatory compliance.

📋 Description

• 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 • 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 • 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 with Data Science teams to improve platform usability, performance, and reliability

🎯 Requirements

• 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: experience with PyTorch, TensorFlow, Hugging Face, or JAX • Preferred: experience with distributed training frameworks such as Ray, DeepSpeed, Horovod, or NCCL • Preferred: experience with vector databases, LLM infrastructure, RAG architectures, or GenAI platforms • Preferred: experience operating inference platforms for large language models • Preferred: experience supporting AI research or Data Science teams in production environments • Preferred: contributions to Cloud Native, Kubernetes, AI, or ML open-source communities • Cloud, Kubernetes, NVIDIA, or AI/ML certifications are a plus

🏖️ Benefits

• Competitive compensation • Top-tier health insurance • Enabling culture • Responsibility and trust • Freedom and autonomy in the role • Fun and dynamic workplace • Opportunity to work alongside leading AI professionals • Inclusive and empowering workplace culture