AI Infrastructure Engineer

Role Overview

• 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 both training pipelines and inference services
• Implement resource scheduling and job management systems across heterogeneous compute environments
• Benchmark system performance, diagnose bottlenecks, and implement improvements across both training and inference infrastructure
• Build monitoring, alerting, and observability solutions tailored to ML workloads running on Kubernetes and Slurm
• Respond swiftly to system outages and collaborate across teams to maintain high uptime for critical training runs and inference services
• Optimize cluster utilization and implement autoscaling strategies for dynamic workload demands

Requirements

• Strong expertise in Kubernetes administration, including custom resource definitions, operators, and cluster management
• Hands-on experience with Slurm workload management, including job scheduling, resource allocation, and cluster optimization
• Experience with deploying and managing distributed training systems at scale
• Deep understanding of container orchestration and distributed systems architecture
• High level familiarity with LLM architecture and training processes (Multi-Head Attention, Multi/Grouped-Query, distributed training strategies)
• Experience managing GPU clusters and optimizing compute resource utilization
• Expert-level Kubernetes administration and YAML configuration management
• Proficiency with Slurm job scheduling, resource management, and cluster configuration
• Python and C++ programming with focus on systems and infrastructure automation
• Hands-on experience with ML frameworks such as PyTorch in distributed training contexts
• Strong understanding of networking, storage, and compute resource management for ML workloads
• Experience developing APIs and managing distributed systems for both batch and real-time workloads
• Solid debugging and monitoring skills with expertise in observability tools for containerized environments
• Experience with Kubernetes operators and custom controllers for ML workloads
• Advanced Slurm administration including multi-cluster federation and advanced scheduling policies
• Familiarity with GPU cluster management and CUDA optimization
• Experience with other ML frameworks like TensorFlow or distributed training libraries
• Background in HPC environments, parallel computing, and high-performance networking
• Knowledge of infrastructure as code (Terraform, Ansible) and GitOps practices
• Experience with container registries, image optimization, and multi-stage builds for ML workloads
• Demonstrated experience managing large-scale Kubernetes deployments in production environments
• Proven track record with Slurm cluster administration and HPC workload management
• Previous roles in SRE, DevOps, or Platform Engineering with focus on ML infrastructure
• Experience supporting both long-running training jobs and high-availability inference services
• Ideally, 3-5 years of relevant experience in ML systems deployment with specific focus on cluster orchestration and resource management

Tech Stack

• Ansible
• Distributed Systems
• Kubernetes
• Python
• PyTorch
• Tensorflow
• Terraform

Benefits

• Flexible work arrangements
• Professional development opportunities