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Open nowPosted 248 days ago

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

Workable (global search)108,016 open roles

Where
Dubai, United Arab Emirates
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Your applicationOpen nowDell AI Infrastructure & MLOps Engineer - (6 Month Only)Workable (global search) · Dubai, United Arab Emirates

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Early applications get read.

7.9% of postings close within 7 days. Measured by our own scanner across the market. Workable (global search) postings stay open a median of 7 days.

Share of postings closed within
  1. 1.6%1 day
  2. 3.6%3 days
  3. 7.9%7 days
  4. 14.9%14 days
  5. 34.0%30 days
This job: posted 248 days ago

Workable (global search) median: 7 days open

The posting

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.

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