We are looking for an AI Infra engineer to join our growing team. We work with Kubernetes, Slurm, Python, C++, Py Torch, 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 Py Torch 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 Tensor Flow or distributed training libraries

• Background in HPC environments, parallel computing, and high-performance networking

• Knowledge of infrastructure as code (Terraform, Ansible) and Git Ops 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