AI Infrastructure Engineer – Member of Technical Staff

About the role

We are looking for an AI Infra engineer to join our growing team. The role focuses on building, deploying, and optimizing large‑scale AI training and inference clusters on Kubernetes, Slurm and AWS.

Key responsibilities

• Design, deploy, and maintain scalable Kubernetes clusters for AI model inference and training workloads.
• Manage and optimize Slurm‑based HPC environments for distributed training of large language models.
• Develop robust APIs and orchestration systems for training pipelines and inference services.
• Implement resource scheduling and job management across heterogeneous compute environments.
• Benchmark performance, diagnose bottlenecks, and improve both training and inference infrastructure.
• Build monitoring, alerting, and observability solutions tailored to ML workloads.
• Respond to system outages and collaborate to maintain high uptime for critical runs.
• Optimize cluster utilization and implement autoscaling strategies for dynamic workloads.

Required profile

• Strong expertise in Kubernetes administration, including custom resources, operators, and cluster management.
• Hands‑on experience with Slurm workload management and HPC cluster optimization.
• Deep understanding of distributed training systems and LLM architectures.
• Experience managing GPU clusters and optimizing compute resource utilization.
• Background in SRE, DevOps, or Platform Engineering for ML infrastructure.

Required skills

• Kubernetes administration and YAML configuration.
• Slurm job scheduling and resource management.
• Python and C++ programming for systems automation.
• Distributed training with PyTorch (and optionally TensorFlow).
• Networking, storage, and compute resource management for ML workloads.
• Observability tools for containerized environments.
• Infrastructure as code (Terraform, Ansible) and GitOps practices.