MLOps Engineer — On-Prem Data Infrastructure

MARSS Group; Nice; Sur place; EUR 70 000 - 100 000; Plein temps

Résumé du poste

MARSS Group is seeking an experienced MLOps Engineer to build and operate the data infrastructure supporting our ML environment in Nice, France. This hands-on role sits at the intersection of data engineering, DevOps and infrastructure, focusing on the data side of the ML platform rather than model training.

You will provision bare-metal and VM environments, automate data workflows, and ensure offline operation with minimal internet access.

Qualifications

• Hands-on Linux server and infrastructure experience.
• Strong scripting with Python for automation.
• Experience building offline or restricted-network environments.
• Proficient with containerized workflows using Docker.
• Familiarity with ML lifecycle tools (MLflow, DVC, ClearML).
• Ability to design and implement scalable ML data infrastructure.

Responsabilités

• Design, configure and maintain servers, VMs and environments for data pipelines and ML development.
• Build and maintain data collection, preparation, validation, versioning and availability infrastructure.
• Automate data workflow from collection through to training triggers without manual steps.
• Configure Linux environments and administer ML and data team servers.
• Deploy and maintain ML/data management platforms like MLflow, DVC or equivalent.
• Operate offline-capable environments with local mirrors and private registries.
• Support dataset/versioning, experiment traceability and monitoring.
• Collaborate with ML, Data and Software Engineers to connect pipelines to training and inference setups.

Connaissances

Linux

Docker

Python

CI/CD

MLOps

Data pipelines

Outils

MLflow

DVC

ClearML

Description du poste

MARSS Group is seeking an experienced MLOps Engineer to build and operate the data infrastructure supporting our ML environment in Nice, France. This hands-on role sits at the intersection of data engineering, DevOps and infrastructure, focusing on the data side of the ML platform rather than model training.

You will provision bare-metal and VM environments, automate data workflows, and ensure offline operation with minimal internet access.