Dell AI Infrastructure & MLOps Engineer - (6 Month Only)
At a glance
Mid-level ML Platform & Ops role at Müller`s Solutions. Dubai.
Pay not stated ML Platform & Ops salary
aimlroles summary, based on the employer's posting.
What you'll do
- Run AI infrastructure and MLOps platforms in production, keeping their operation and upkeep on track
- Watch Kubernetes AI workloads and resolve issues affecting system stability, speed or availability
- Plan and carry out acceptance tests for AI infrastructure and platforms
- Install NVIDIA Enterprise AI Stack, configure Kubeflow or MLflow, and help build workflows that include data pipelines
- Advise the customer hands-on during the AI environment’s initial setup and implementation
What you bring
- Four to six years of practical Python and Jupyter Notebook or JupyterLab work
- Hands-on familiarity with NVIDIA Enterprise AI Stack, Ubuntu Linux, Kubernetes, Kubeflow or MLflow, and QFLOW
- Experience with enterprise infrastructure, including Dell PowerScale, XE or R570 servers, network switches, and GPU-based AI servers
- You can write, test and maintain operational scripts and AI workflows
Who this fits
This is a six-month, operations-heavy individual contributor contract based on site in Dubai. It suits someone comfortable with hands-on infrastructure setup, production support and customer-facing technical guidance.
From the employer
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.