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

Mullers Solutions 1; Dubai; On-site; AED 391,000 - 614,000; Full time

Job summary

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

You will manage, operate, and guide deployment of a strategic AI environment, working closely with the customer as a technical advisor and hands-on engineer, with duties including monitoring and maintaining AI workloads and setting up

Qualifications

• 4–6 years of practical experience in Python and Jupyter Notebook/JupyterLab.
• Strong Kubernetes experience and familiarity with Kubeflow/MLflow.
• Proven ability to install, configure, and operate AI infrastructure and workflows.

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.

Skills

Python

Jupyter Notebook / JupyterLab

Kubernetes

Kubeflow

MLflow

NVIDIA Enterprise AI Stack

Ubuntu Linux

Tools

QFLOW

Kubeflow

MLflow

Job description

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.

What about the role 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.

Requirements

What should you have to fit in this role?

Technical Requirements

AI / MLOps Stack

• 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)

Programming & Automation

• 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)
• GPU-based AI servers (in a small-scale environment)

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

To succeed in this role, it's 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.