MLOps Engineer – Data Infrastructure

🔥 11 minutes ago

🇫🇷 France – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🚰 Data Engineer

👻 Ghost score 12%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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MARSS Group

WebsiteLinkedInAll Job Openings

51 - 200 employees

📦 Logistics

💼 Consulting

🎖️ Defense

Logistics • Consulting • Defense

MARSS Group is a technology company specializing in the development of advanced security and surveillance systems aimed at enhancing national security. Founded in 2005, the company's expertise is rooted in over 15 years of research and collaboration with EU, NATO, and various defense agencies. MARSS's technological innovations include integrated sensor surveillance, artificial intelligence, and open-source intelligence, which are utilized to protect critical infrastructure, naval assets, special forces, and high-profile individuals globally. Their product lineup features systems such as NiDAR™, advancing command and control functionality, and various solutions designed for counter-unmanned aerial systems and situational awareness in different security contexts.

📋 Description

• Build and operate infrastructure supporting the company's data and Machine Learning environment • Provision and own bare-metal and virtual machines and the environments running on them • Design, configure and maintain servers, VMs and environments supporting data pipelines and ML development • Build and maintain infrastructure and automation for data collection, preparation, validation, versioning and availability • Automate data flow from collection through preparation to downstream processes, including training triggers • 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 • Build reproducible offline environments with local package mirrors, private container registries and offline artefact management • Implement dataset and artefact versioning and support experiment traceability • Operate the orchestration layer as a deployed service, including installation, configuration, resource management, upgrades and log/metric plumbing • Support management of large and varied datasets, including images, video, structured data and temporal/time-series data • Integrate data pipelines with existing training and inference setup alongside the Data Engineer • Containerise data applications using Docker and integrate them into deployment tooling • Develop automation for environment provisioning, testing, deployment and monitoring • Define and implement infrastructure and service monitoring, including data-quality and pipeline-health monitoring • Monitor infrastructure and ML workloads and troubleshoot performance, availability and configuration issues • Support efficient use of compute and storage resources • Contribute to the architecture and continuous improvement of the 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