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