DevOps Engineer, GPU

METALINE

. Industrialize and automate the deployment of AI platforms in secure on-premises environments .

Posted 9/21/2026full-timeGuyancourt • FranceMid-LevelSenior💰 €51,000 - €60,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in managing and automating AI/ML platform deployments, with a strong focus on security, compliance, and operational excellence. Proficient in leveraging DevOps methodologies and tools to enhance deployment processes and maintain production environments.

Highest-signal resume keywords

DevOps ManagementKubernetes OrchestrationAI/ML Platform OperationsCI/CD ProficiencyOn-Premises Infrastructure Knowledge

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

PythonBash ScriptingKubernetesOpenShiftDockerGitLab CI/CDJenkinsPrometheusGrafanaAI/ML Architectures

Soft Skills

CollaborationPresentation SkillsAgile Methodologies

Tools & Technologies

Run.aiSlurmClearMLVolcanoNVIDIA GPU OperatorELK StackDynatraceAtlassian Tools

Certifications & Qualifications

CKACKADOpenShift Certification

Industry Keywords

BankingRegulated IndustryData AutomationOperational Maintenance

Tech Stack

Tools & technologies

CloudDockerGrafanaJenkinsKubernetesOpenShiftPrometheusPythonPyTorchTensorflow

About the role

Key responsibilities & impact

Industrialize and automate the deployment of AI platforms in secure on-premises environments
Contribute to the evolution of GPUaaS and LLMaaS platform architecture
Implement managed services while integrating security, compliance, and governance requirements
Oversee production deployment, documentation, and the operational and security maintenance of AI platforms
Improve and automate deployment and operations processes
Deliver presentations and demonstrations of the solutions and services developed
Collaborate as part of a DevOps team in an Agile environment
Participate in Agile ceremonies, roadmap planning, and backlog management
Participate in the team’s technical on-call rotation

Requirements

What you’ll need

Significant experience in DevOps and the management of AI/ML platforms
Hands-on experience managing and orchestrating GPU resources, ideally with Kubernetes / OpenShift
Proficiency in Kubernetes, OpenShift, and Docker
Good knowledge of on-premises infrastructure and familiarity with cloud environments
Experience with Run.ai, Slurm, ClearML, or Volcano
Knowledge of NVIDIA GPU Operator and MLOps environments
Proficiency in GitLab CI/CD and Jenkins
Good knowledge of Prometheus, Grafana, the ELK Stack, or Dynatrace
Proficiency in Python and Bash scripting
Knowledge of AI/ML architectures and the Transformers, TensorFlow, and PyTorch frameworks
Proficiency with Atlassian tools (Jira, Wiki)
Professional working proficiency in English, with regular use in a professional context
Higher education degree in computer science, Master’s level (Bac+5 / M2) or equivalent
6 to 10 years of experience, including at least 5 years in data and IT process automation
Significant experience operating and maintaining production platforms in operational condition
CKA/CKAD or OpenShift certification is a plus
Experience with Kestra is desirable
Experience in banking or another regulated industry is desirable