AI Infrastructure & MLOps Engineer

Responsibilities

• Operate and maintain AI infrastructure and MLOps platforms in a production environment
• Monitor, manage, and troubleshoot Kubernetes-based AI workloads
• Perform Acceptance Testing Planning and Execution for AI infrastructure and platforms
• Ensure stability, performance, and availability of AI systems
• Support day-to-day operational tasks across compute, storage, and networking layers
• Install and configure NVIDIA Enterprise AI Stack (NVAI)
• Configure and manage MLOps platforms such as Kubeflow and MLflow
• Assist in setting up end-to-end AI workflows, including data pipelines
• Act as a technical guide and advisor to the customer during early AI adoption stages

Requirements

• Proficient experience with the NVIDIA Enterprise AI Stack
• Experience with Kubernetes and Ubuntu Linux
• Knowledge of Kubeflow, MLflow, and QFLOW
• 4–6 years of practical experience with Python and Jupyter Notebook/JupyterLab
• Practical experience with Dell PowerScale, XE Servers, Dell R570 Servers, and Dell Network Switches
• Experience with GPU-based AI servers

Preferred Qualifications

• Familiarity with data frameworks like Apache Spark or Hadoop
• Understanding of ML model monitoring and logging practices
• Experience with security best practices in AI systems