Description

You will design and operate ML training and deployment pipelines for LLM applications on Azure and SUSE Linux GPU clusters.

This role is on-site.

Responsibilities

• Manage SUSE Linux Enterprise (SLES) GPU clusters with NVIDIA H100 hardware, including driver installation and CUDA/NCCL tuning.
• Deploy GPU-based inference endpoints using Managed Online Endpoints, AKS GPU node pools, or Arc-enabled Kubernetes, handling traffic splits and rollbacks.
• Automate CI/CD pipelines in Azure DevOps from data preparation to model deployment using Infrastructure as Code (Terraform/Bicep).
• Integrate MLflow, Azure ML model registry, and Model Catalog for unified model versioning and promotion.
• Monitor model performance, data drift, and GPU metrics via Azure Monitor, Log Analytics, and NVIDIA DCGM Exporter.

Required Skills

• 7+ years in ML/AI engineering or MLOps with significant GPU workload experience.
• Hands-on experience with SUSE Linux (SLES) in production AI environments.
• In-depth knowledge of NVIDIA H100 architecture (HBM3, NVLink, MIG, multi-GPU orchestration).
• Proficiency in Azure ML, Azure AI Foundry, and Prompt Flow for LLM workflows.
• Expertise in deploying on Kubernetes with GPU node support (AKS, Arc-enabled K8s).
• Experience with Infrastructure as Code, specifically Terraform and Bicep.
• Familiarity with CI/CD workflows using Azure DevOps Pipelines.
• Knowledge of distributed training frameworks (DeepSpeed, Horovod, PyTorch DDP).
• Experience implementing governance and security for ML platforms.

Key Skills

Education

Any Graduate