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.