Airbus Defence and Space SAU
Aerospace
AIPlatformEngineer
Neural analysis suggests this role is
optimal for Professional candidates.
“AI Platform Engineer at Airbus Defence and Space SAU. Skills: AI Platform Management, Infrastructure Automation, High-Security Environment Operations, Computing Resource Optimization, Storage and Network Architecture, Observability and Continuity, System Security. Design and administer Kubernetes environments optimized for Artificial Intelligence workloads. Implement Infrastructure as Code (IaC) methodologies and continuous deployment models (GitOps) to ensure system reproducibility”
What You'll Achieve.
Build and scale high-performance computing infrastructure; Ensuring a stable, automated, and scalable working environment; Ensure system reproducibility; Ensure data integrity and speed; Ensure cluster health, GPU performance, and proactive incident detection; Protect critical infrastructure
Industry & Context.
High-security environments, Air-gapped or offline environments, Maximum-security scenarios
What They're Looking For.
Must Have
Solid experience (3+ years) in DevOps, SRE, or Platform Engineering roles, Degree in Computer, Telecomunications, Maths or Software Engineering, Proven experience working with container orchestration (Kubernetes), Experience managing critical infrastructure or isolated environments (air-gapped/offline), Advanced proficiency in Python, Advanced proficiency in Linux operating systems and network administration, Experience in deployment automation (Ansible, Terraform, or similar tools), Ability to work with modern deployment methodologies (GitOps), B2 level in English
Nice to Have
Military Avionics and embedded/Real Time Software knowledge is desirable, Previous experience managing infrastructure for Artificial Intelligence (NVIDIA/CUDA driver management), Knowledge of distributed storage solutions and private image registry management, Official Kubernetes certifications (CKA/CKS), Interest in working on defense projects and cutting-edge technology within high-security environments
What You'll Do.
Design and administer Kubernetes environments optimized for Artificial Intelligence workloads
Implement Infrastructure as Code (IaC) methodologies and continuous deployment models (GitOps) to ensure system reproducibility
Manage air-gapped (isolated) infrastructures
ensuring local repository management
and security without reliance on the public cloud
Administer and prepare high-performance nodes with GPU acceleration for inference and training tasks
Configure and maintain persistent storage systems and segmented networks to ensure data integrity and speed
Implement advanced monitoring systems to ensure cluster health
and proactive incident detection
Apply hardening policies and access control to protect critical infrastructure
How You'll Work.
Team & Collaboration
Join the Architecture & Integration team (TADMC2)
Communication Scope
B2 level in English
Full Job Description
****Job Description:**** **Airbus Defence & Space is looking for an AI Platform Engineer (MLOps) to****build and scale****high-performance computing infrastructure in high-security environments.** TADMC provides full engineering and software support for CLAEX's Operational Flight Programs (OFP) and in-service aircraft fleets . The selected candidate will join the Architecture & Integration**** team (TADMC2) at CLAEX, in Torrejón de Ardoz Air Base. Their primary mission will be to design, operate, and evolve the Kubernetes platform dedicated to Artificial Intelligence, ensuring a stable, automated, and scalable working environment built on cutting-edge infrastructure (GPU). We are looking for a specialist in **Platform Engineering / MLOps** who thrives on the challenges of critical infrastructure. We are not just looking for someone to maintain systems, but for a professional capable of building and automating complex computing environments in maximum-security scenarios (air-gapped or offline environments). **Key Responsibilities** * **AI Platform Management:** Design and administer Kubernetes environments optimized for Artificial Intelligence workloads. * **Infrastructure Automation:** Implement Infrastructure as Code (IaC) methodologies and continuous deployment models (GitOps) to ensure system reproducibility. * **High-Security Environment Operations:** Manage air-gapped (isolated) infrastructures, ensuring local repository management, updates, and security without reliance on the public cloud. * **Computing Resource Optimization:** Administer and prepare high-performance nodes with GPU acceleration for inference and training tasks. * **Storage and Network Architecture:** Configure and maintain persistent storage systems and segmented networks to ensure data integrity and speed. * **Observability and Continuity:** Implement advanced monitoring systems to ensure cluster health, GPU performance, and proactive incident detection. * **System Security:** Apply hardenin
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