Strong expertise in Kubernetes administration, including custom resource definitions and cluster management.
Hands-on experience with Slurm workload management and cluster optimization.
Experience deploying and managing distributed training systems at scale.
Deep understanding of container orchestration and distributed systems architecture.
High familiarity with LLM architecture and training processes.
Experience managing GPU clusters and optimizing compute resource utilization.
Responsibilities
Design, deploy, and maintain scalable Kubernetes clusters for AI workloads.
Manage and optimize Slurm HPC environments for distributed training.
Develop APIs and orchestration systems for training pipelines and inference services.
Implement resource scheduling and job management in diverse compute environments.
Benchmark system performance and resolve bottlenecks in infrastructure.
Build monitoring and observability solutions for ML workloads.
Respond to system outages and maintain high uptime for services.
Optimize cluster utilization and implement autoscaling for workload demands.
Benefits
Collaborative working environment with cutting-edge technologies.
Opportunity to work on large-scale AI model training and inference.
Engagement with interdisciplinary teams including Research and Inference.
Access to resources for professional development and training.
Flexibility in work arrangements and problem-solving approaches.
Full Job Description
We are looking for an AI Infra engineer to join our growing team. We work with Kubernetes, Slurm, Python, C++, PyTorch, and primarily on AWS. As an AI Infrastructure Engineer, you will be partnering closely with our Inference and Research teams to build, deploy, and optimize our large-scale AI training and inference clusters 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 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
Qualifications
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
Required Skills
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
Preferred Skills
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
Required Experience
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
