Müllers Solutions

AI Infrastructure & MLOps Engineer

Applied Machine LearningConfirmed live in the last 24 hours

Full-timeDubai - United Arab EmiratesNo salary listed

About the Job

Requirements

Proficient experience with the NVIDIA Enterprise AI Stack.
Familiarity with Ubuntu Linux.
Experience with Kubernetes.
Knowledge of Kubeflow and MLflow.
Experience with QFLOW, an open-source AI data pipeline management tool.
Four to six years of practical experience with Python.
Four to six years of practical experience with Jupyter Notebook or JupyterLab.
Competence in writing, testing, and maintaining operational scripts and AI workflows.
Practical experience with enterprise infrastructure, including Dell PowerScale, XE Server, Dell R570 Servers, Dell Network Switches, and GPU-based AI servers in a small-scale environment.

Responsibilities

Operate and maintain AI infrastructure and MLOps platforms in a production environment.
Monitor, manage, and troubleshoot Kubernetes-based AI workloads.
Plan and execute acceptance testing for AI infrastructure and platforms.
Ensure the 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 AI adoption.

Desired Qualifications

Familiarity with data frameworks such as Apache Spark or Hadoop for data processing.
Understanding of machine-learning model monitoring and logging practices to ensure system reliability.
Experience with security best practices in AI systems.

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

Müller`s Solutions · Dubai, United Arab Emirates · — · Posted 2026-01-29

Workplace: on_site

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.