{"id":2207906,"url":"https://alion.io/job/marss-fr-mlops-engineer-data-infrastructure","title":"FR - MLOps Engineer - Data Infrastructure","company":{"id":2490548,"name":"MARSS","domain":"marss.com","url":"https://alion.io/company/marss-com","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"BambooHR","truth_index":{"grade":"B","score":75,"open_postings":4,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-13T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":[],"countries":[],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CI/CD","optional":false},{"name":"ClearML","optional":false},{"name":"Docker","optional":false},{"name":"DVC","optional":false},{"name":"Git","optional":false},{"name":"Linux","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Python","optional":false},{"name":"Time Series Forecasting","optional":false}],"status":"live","first_seen_at":"2026-08-21T00:00:00Z","employer_posted_date":"2026-08-21","last_verified_at":"2026-10-13T10:18:23Z","board_verified":true,"closed_at":null,"days_open":53,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":53},"description":"Job Title: MLOps Engineer - Data Infrastructure\nReportsto: VP AI\nLocation: France - Nice\nType: Full-time\nThe Role\nWe are looking for an experienced MLOps Engineerto build and operate the infrastructure supporting our data and Machine Learning environment.\nThis is a hands-on technical role at the intersection of data engineering, DevOps, and infrastructure.\nYou 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.\nThe 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.\nYou 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.\nYou 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.\nMain Responsibilities\nDesign, configure and maintain the server, VMs and environmentsupporting our data pipelines and ML development.\nBuild and maintain the infrastructure and automation covering data collection, preparation, validation, versioning and availability.\nAutomate the flow from newly collected data through preparation to the point where downstream processes, including training, can be triggered without manual intervention.\nConfigure and administer Linux-based environments and serversused by ML and data teams.\nDeploy, configure and maintain ML/data management platforms such as MLflow, DVC, ClearML or equivalent technologies.\nBuild reproducible environments that function without internet access, including local package mirrors, private container registries and offline artefact management.\nImplement appropriate dataset and artefact versioning, and support experiment traceability.\nOperate 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.\nSupport the management of large and varied datasets, including images, video, structured data and temporal/time-series data.\nWork with the Data Engineer to integrate data pipelines with our existing training and inference setup.\nContainerise data applications using Docker and integrate them into our deployment tooling.\nDevelop automation for environment provisioning, testing, deployment and monitoring.\nDefine and implement infrastructure and service monitoring, and implement the data-quality and pipeline-health monitoring defined by the Data Engineer\nMonitor infrastructure and ML workloads and troubleshoot performance, availability and configuration issues.\nSupport efficient use of compute and storage resources for data processing and storage.\nContribute to the architecture and continuous improvement of the company's internal ML platform.\nDocument infrastructure, configurations, deployment processes and operational procedures.\nRequirements\nStrong professional experience in MLOps, DevOps, data infrastructure or a closely related engineering role.\nStrong hands-on knowledge of Linux, including confidence working extensively from the command line.\nExperience configuring and managing servers, virtual machines and technical infrastructure.\nStrong experience with Docker and containerised environments.\nExperience building environments that work offline or under restricted network conditions: local registries, mirrors, or disconnected installations.\nPractical experience with ML lifecycle/data management tools such as MLflow, DVC, ClearML or comparable frameworks.\nGood Python skills, particularly for scripting, automation and integration.\nGood understanding of data pipelines and the requirements associated with large and heterogeneous datasets.\nGood understanding of Machine Learning development and deployment workflows.\nExperience implementing CI/CD or similar automation for software, data or ML workloads.\nGood understanding of Git and software development workflows.\nStrong troubleshooting skills across software, infrastructure and configuration issues.\nAbility to independently design and implement technical solutions rather than only operate an existing platform.\nFluent English, written and spoken.","description_format":"text","description_chars":5060,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[{"language":"English","level":"Advanced (C1)","optional":false}]},"benefits":[],"hiring_locations":[{"name":"France","iso":"FR","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Artificial Intelligence","Information Technology","IoT Platforms & Solutions"],"lifecycle":[{"event":"open","at":"2026-10-10T00:28:42Z"}],"visa":[],"liveness":{"score":14,"band":"cold","label":"Long shot","p_open":1,"p_active":0.388,"p_room":0.35,"age_days":53,"expected_fill_days":20,"reasons":["conf:2","win:tail"],"computed_at":"2026-10-13T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/marss-fr-mlops-engineer-data-infrastructure","json_url":"https://alion.io/job/marss-fr-mlops-engineer-data-infrastructure.json","meta":{"generated_at":"2026-10-13T11:44:20Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per 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