Description

You will design and implement ML training, evaluation, and deployment pipelines for LLM applications.

Responsibilities

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

Required Skills

• 7+ years in ML/AI engineering or MLOps roles 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.

Preferred Skills

• Proven track record deploying ML models in hybrid (cloud + on-prem) environments.
• Experience applying Azure’s Well-Architected ML guidance.

Key Skills

Education

Any Graduate