Job Title: MLOps Engineer - Data Infrastructure
Reports to: VP AI
Location: France - Nice
Type: Full-time
The Role
We are looking for an experienced MLOps Engineer to build and operate the infrastructure supporting our data and Machine Learning environment.
This is a hands-on technical role at the intersection of data engineering, DevOps, and infrastructure.
You will be responsible for helping us build a reliable in-house environment for managing data, versioning datasets, automating data workflows and connecting them to our existing training and deployment setup. The focus of this role is the data side of our ML platform: you will not be training or evaluating models yourself.
The role requires someone very comfortable working directly with Linux servers and infrastructure, rather than someone whose experience is limited to using high-level cloud ML services. Our platform runs on our own infrastructure, and some of our environments operate offline for extended periods with only intermittent connectivity. Everything must work without relying on internet access at runtime.
You will provision and own the bare-metal and virtual machines and environments that run on it, working in tandem with DevOps on ML infrastructure. You will be maintaining the data pipeline layers of the deployment tooling.
You will work closely with our Machine Learning, Data and Software Engineers to build the infrastructure for the AI team. This role works alongside a Data Engineer who designs the data architecture and pipelines; you own the infrastructure those pipelines run on and the automation that connects them to the rest of the platform, including the monitoring of the infrastructure. Both roles report directly to the VP AI.
Main Responsibilities
- Design, configure and maintain the server, VMs and environment supporting our data pipelines and ML development.
- Build and maintain the infrastructure and automation covering data collection, preparation, validation, versioning and availability.
- Automate the flow from newly collected data through preparation to the point where downstream processes, including training, can be triggered without manual intervention.
- Configure and administer Linux-based environments and servers used by ML and data teams.
- Deploy, configure and maintain ML/data management platforms such as MLflow, DVC, ClearML or equivalent technologies.
- Build reproducible environments that function without internet access, including local package mirrors, private container registries and offline artefact management.
- Implement appropriate dataset and artefact versioning, and support experiment traceability.
- Operate the orchestration layer as a deployed service: installation, configuration, resource management, upgrades and log/metric plumbing, while the Data Engineer defines the workflows that run on it.
- Support the management of large and varied datasets, including images, video, structured data and temporal/time-series data.
- Work with the Data Engineer to integrate data pipelines with our existing training and inference setup.
- Containerise data applications using Docker and integrate them into our deployment tooling.
- Develop automation for environment provisioning, testing, deployment and monitoring.
- Define and implement infrastructure and service monitoring, and implement the data-quality and pipeline-health monitoring defined by the Data Engineer
- Monitor infrastructure and ML workloads and troubleshoot performance, availability and configuration issues.
- Support efficient use of compute and storage resources for data processing and storage.
- Contribute to the architecture and continuous improvement of the company's internal ML platform.
- Document infrastructure, configurations, deployment processes and operational procedures.
Requirements
- Strong professional experience in MLOps, DevOps, data infrastructure or a closely related engineering role.
- Strong hands-on knowledge of Linux, including confidence working extensively from the command line.
- Experience configuring and managing servers, virtual machines and technical infrastructure.
- Strong experience with Docker and containerised environments.
- Experience building environments that work offline or under restricted network conditions: local registries, mirrors, or disconnected installations.
- Practical experience with ML lifecycle/data management tools such as MLflow, DVC, ClearML or comparable frameworks.
- Good Python skills, particularly for scripting, automation and integration.
- Good understanding of data pipelines and the requirements associated with large and heterogeneous datasets.
- Good understanding of Machine Learning development and deployment workflows.
- Experience implementing CI/CD or similar automation for software, data or ML workloads.
- Good understanding of Git and software development workflows.
- Strong troubleshooting skills across software, infrastructure and configuration issues.
- Ability to independently design and implement technical solutions rather than only operate an existing platform.
- Fluent English, written and spoken.