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
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Design, deploy, and maintain scalable Kubernetes clusters for AI model inference and training workloads
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Manage and optimize Slurm-based HPC environments for distributed training of large language models
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Develop robust APIs and orchestration systems for both training pipelines and inference services
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Implement resource scheduling and job management systems across heterogeneous compute environments
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Benchmark system performance, diagnose bottlenecks, and implement improvements across both training and inference infrastructure
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Build monitoring, alerting, and observability solutions tailored to ML workloads running on Kubernetes and Slurm
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Respond swiftly to system outages and collaborate across teams to maintain high uptime for critical training runs and inference services
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Optimize cluster utilization and implement autoscaling strategies for dynamic workload demands
Qualifications
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Strong expertise in Kubernetes administration, including custom resource definitions, operators, and cluster management
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Hands-on experience with Slurm workload management, including job scheduling, resource allocation, and cluster optimization
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Experience with deploying and managing distributed training systems at scale
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Deep understanding of container orchestration and distributed systems architecture
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High level familiarity with LLM architecture and training processes (Multi-Head Attention, Multi/Grouped-Query, distributed training strategies)
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Experience managing GPU clusters and optimizing compute resource utilization
Required Skills
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Expert-level Kubernetes administration and YAML configuration management
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Proficiency with Slurm job scheduling, resource management, and cluster configuration
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Python and C++ programming with focus on systems and infrastructure automation
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Hands-on experience with ML frameworks such as PyTorch in distributed training contexts
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Strong understanding of networking, storage, and compute resource management for ML workloads
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Experience developing APIs and managing distributed systems for both batch and real-time workloads
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Solid debugging and monitoring skills with expertise in observability tools for containerized environments
Preferred Skills
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Experience with Kubernetes operators and custom controllers for ML workloads
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Advanced Slurm administration including multi-cluster federation and advanced scheduling policies
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Familiarity with GPU cluster management and CUDA optimization
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Experience with other ML frameworks like TensorFlow or distributed training libraries
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Background in HPC environments, parallel computing, and high-performance networking
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Knowledge of infrastructure as code (Terraform, Ansible) and GitOps practices
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Experience with container registries, image optimization, and multi-stage builds for ML workloads
Required Experience
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Demonstrated experience managing large-scale Kubernetes deployments in production environments
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Proven track record with Slurm cluster administration and HPC workload management
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Previous roles in SRE, DevOps, or Platform Engineering with focus on ML infrastructure
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Experience supporting both long-running training jobs and high-availability inference services
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Ideally, 3-5 years of relevant experience in ML systems deployment with specific focus on cluster orchestration and resource management