Senior AI Infrastructure Architect (GCP /Kubernetes /Vertex AI /NVIDIA)

We are looking for a Senior AI Infrastructure Architect to design and build cloud-native, GPU-enabled AI infrastructure and large-scale MLOps platforms for an innovative project in the security and urban intelligence sector.

The consultant will be responsible for designing scalable Kubernetes environments, building and optimizing MLOps infrastructure, and supporting high-performance AI/ML model training and deployment.

Responsibilities

• Design and implement GPU-enabled Kubernetes clusters for scalable AI workloads.
• Architect and optimize MLOps pipelines and container orchestration platforms.
• Configure and manage high-performance cloud environments using Google Cloud Platform (GCP) and Google Kubernetes Engine (GKE).
• Support large-scale AI/ML model training, inference, and production deployment.
• Integrate NVIDIA acceleration technologies to improve AI model performance.
• Work with Vertex AI and other cloud-native AI/ML services.
• Define infrastructure architecture, technical standards, and deployment best practices.

• Collaborate closely with AI, Machine Learning, DevOps, and Engineering teams.

   

Requirements

• Senior-level experience in AI Infrastructure, Cloud Architecture, or MLOps Platform Engineering.
• Deep expertise in Kubernetes, containerization, and cloud-native infrastructure.
• Strong hands-on experience with Google Cloud Platform (GCP), particularly Google Kubernetes Engine (GKE).
• Practical experience with Vertex AI.
• Proven experience designing, building, and operating large-scale MLOps platforms.
• Strong understanding of AI/ML model training, deployment, inference, and production operations.
• Deep knowledge of the NVIDIA AI ecosystem, including:
  • CUDA
  • TensorRT
  • Triton Inference Server
  • NVIDIA NIM
• Experience designing GPU-enabled infrastructure for high-performance AI workloads.

• English level: B2 or higher.

   

Project Details

• Industry: Security & Urban Intelligence
• Engagement: Full-time
• Project Duration: 6–8 months
• Project Start: Within 2–3 weeks

• Location: Europe (Remote)