Dell AI Infrastructure & MLOps Engineer - (6 Month Only)

Müller`s Solutions; United Arab Emirates; On-site; AED 334,800 - 535,680; Full time

Job summary

Müller’s Solutions in the United Arab Emirates seeks an AI Infrastructure & MLOps Engineer for a 6‑month contract. The role is operations‑driven (90%), with hands‑on implementation, configuration, and setup of AI infrastructure and MLOps workflows.

You will guide deployment, monitor AI workloads, and collaborate closely with the customer as a technical advisor. Responsibilities include operating AI infra, managing Kubernetes workloads, testing AI platforms, ensuring system stability, and

Qualifications

• 4–6 years of hands-on Python experience.
• Experience with Jupyter Notebook/JupyterLab environments.
• Experience deploying AI infra and MLOps workflows.
• Proficient in writing and maintaining operational scripts.
• Experience with Kubernetes based AI workloads.

Responsibilities

• Operate and maintain AI infrastructure and MLOps platforms in a production environment.
• Monitor, manage, and troubleshoot Kubernetes-based AI workloads.
• Perform testing planning and execution for AI infrastructure.
• Ensure stability, performance and availability of AI systems.
• Support day-to-day tasks across compute, storage and networking.
• 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 and data pipelines.
• Support initial implementation phase of the AI environment.
• Act as technical advisor to the customer during early AI adoption.

Skills

Python

Jupyter Notebook

JupyterLab

Operational scripting

AI workflows

Kubernetes

Kubeflow

MLflow

QFLOW

NVIDIA Enterprise AI Stack

Ubuntu Linux

Tools

NVIDIA Enterprise AI Stack

Kubeflow

MLflow

QFLOW

Job description

Overview

As an AI Infrastructure & MLOps Engineer at Müller’s Solutions for a 6‑month contract, this role is primarily operations‑focused (90%) with hands‑on involvement in implementation, configuration, and setup of AI infrastructure and MLOps workflows.

You will play a key role in managing, operating, and guiding the deployment of a strategic AI environment, working closely with the customer as a technical advisor and hands‑on 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.
• Support the initial implementation phase of the AI environment.
• Act as a technical guide and advisor to the customer during the early stages of their AI adoption.

Technical Requirements

• Proficient experience with the NVIDIA Enterprise AI Stack.
• Familiarity with Ubuntu Linux.
• Experience with Kubernetes.
• Knowledge of Kubeflow / MLflow.
• Experience with QFLOW (an open‑source AI data pipeline management tool).
• 4–6 years of practical experience in Python, Jupyter Notebook / JupyterLab.
• Competence in writing, testing, and maintaining operational scripts and AI workflows.

Infrastructure Experience

• Practical experience with enterprise infrastructure, encompassing Dell PowerScale (5 nodes), XE Server (1 node), Dell R570 Servers (5 nodes), Dell Network Switches (2 switches), and GPU‑based AI servers.

Environment Overview

Initial implementation of AI Compact configuration: 1 GPU server, 1 PowerScale, 5 control plane servers. Opportunity to shape best practices from the ground up.

Nice to Have

• Familiarity with data frameworks like Apache Spark or Hadoop for data processing.
• Understanding of ML model monitoring and logging practices to ensure system reliability.
• Experience with security best practices in AI systems.