Job description

About Mozn MOZN is a leading Enterprise AI company enabling organizations to make informed decisions in two critical domains: Financial Crime Prevention and Enterprise Knowledge Intelligence.
We’re a diverse, collaborative team of innovators united by a shared purpose: to build AI that delivers tangible business value, builds trust, and empowers people and organizations with augmented intelligence.
Our culture is built on the relentless pursuit of excellence and meaningful impact.
If you’re passionate about working alongside exceptional talent on world-class AI, and you want the autonomy and runway to do the best work of your career, join us in shaping the future of intelligent enterprises.
About the role We are looking for a highly motivated AI Infrastructure Engineer III to join our Cloud Engineering team.
The ideal candidate is passionate about building scalable AI infrastructure that enables machine learning engineers and data scientists to efficiently develop, train, deploy, and operate AI models.
This role focuses on designing, operating, automating, and continuously improving AI platform capabilities including Kubeflow, MLflow, GPU infrastructure, distributed training platforms, model serving infrastructure, and MLOps tooling across cloud-native and hybrid environments.
The ideal candidate possesses deep expertise in AI infrastructure and GPU platforms.
Kubernetes and cloud platform experience are essential enablers, but the primary focus is building highly optimized infrastructure for AI and machine learning workloads.
What you'll do 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.
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 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.