Requirements

6+ years infrastructure engineering experience, GCP/OpenShift, Kubernetes, Docker, ML/LLMOPs, Python, SQL

Skills

KubernetesPythonDocker

Design, implement, and manage high-performance computing environments tailored for AI and machine learning applications
Deploy and maintain GPU-accelerated clusters, cloud-based AI platforms, and parallel processing systems
Collaborate with data scientists and ML engineers to understand infrastructure requirements
Optimize resource allocation and scalability of AI infrastructure for large datasets and complex models
Automate infrastructure provisioning and deployment using Infrastructure as Code (IaC) tools
Ensure security, compliance, and reliability of AI infrastructure
Monitor system performance and troubleshoot issues to minimize downtime
Stay updated on emerging technologies and propose continuous improvements

Bachelor’s or higher degree in Computer Science, Engineering, or related technical field
6+ years of experience in infrastructure engineering, focusing on AI, ML, or HPC
Proficiency with GCP/OpenShift, Kubernetes (k8s), and Docker
Experience with Model training, testing/evaluation, and deployment
Knowledge of ML/LLMOPs and GenAI core mechanics
Expertise in inference scaling, distributed computing, and benchmarking
Experience with GPUs, distributed workloads, and autoscaling
Familiarity with NVIDIA NIMs, Huggingface, and NVIDIA Superpods (HPC, slurm, k8s)
Experience with DevOps tools including CI/CD, argoCD, git, and Jenkins
Proficiency in Python and SQL