ML/AI Infrastructure Engineer

Your tasks:

• Design, deploy and optimize GPU-based AI/ML systems.
• Manage infrastructure for ML/AI – GPU servers, storage, high-throughput networking.
• Optimize environments for model training – hardware selection, cluster configuration (e.g. Kubernetes).
• Performance tuning of AI workloads.
• Implement MLOps practices and CI/CD pipelines for ML workflows.
• Collaborate with data scientists and ML engineers on infrastructure needs.
• Document infrastructure and operational procedures.
• Stay current with GPU technologies, AI frameworks and best practices.