Senior AI Infrastructure Engineer, Llm/ai Platforms

CrowdStrike CrowdStrike · Enterprise · United States · Remote

Senior AI Infrastructure Engineer role focused on building and optimizing LLM infrastructure for training, fine-tuning, and inference. The role involves provisioning GPU clusters, developing model-serving infrastructure, leading model lifecycle management, designing evaluation frameworks, optimizing GPU utilization, architecting data platforms for LLMs, RAG, and agentic systems, and delivering production-ready code with MLOps/DataOps best practices. Experience with distributed training, inference serving frameworks, containerization, and cloud platforms is expected.

What you'd actually do

  1. Provision and configure large GPU clusters and compute resources for LLM training, finetuning, and inference workloads.
  2. Develop and optimize LLM model-serving infrastructure, including deployment and optimization of various inference frameworks.
  3. Lead model lifecycle management including versioning, checkpointing and reproducibility across training and inference deployments.
  4. Design and champion robust evaluation frameworks to assess model performance, accuracy, and reliability, ensuring AI systems are consistently at production-ready standards.
  5. Architect and maintain data platforms and pipelines specifically designed to support LLMs, Retrieval-Augmented Generation (RAG), and AI Agentic Systems at scale.

Skills

Required

  • Python
  • CUDA
  • NVIDIA drivers
  • GPU compute fundamentals
  • TPU compute fundamentals
  • vLLM
  • Triton Inference Server
  • Pytorch
  • Ray
  • Megatron
  • JAX
  • Docker
  • Kubernetes
  • Slurm
  • Airflow
  • Terraform
  • Ansible
  • AWS
  • GCP
  • OCI
  • MLOps Tools (MLflow, Sagemaker, Vertex AI)
  • LLM infrastructure engineering
  • cluster provisioning
  • optimizing training workloads
  • maintaining inference pipelines
  • clean, elegant, performant, and well-tested code
  • engineering practices
  • resilient architecture design
  • technical leadership
  • mentorship capabilities
  • AI technologies

Nice to have

  • security industry experience
  • building, deploying, and managing LLMs in a production environment
  • cybersecurity, intelligence, or high-compliance industries

What the JD emphasized

  • LLM infrastructure
  • large scale training pipelines
  • scalable AI-powered systems
  • extremely large-scale systems
  • LLM model-serving infrastructure
  • LLM training
  • LLM infrastructure engineering
  • LLM-based systems and applications

Other signals

  • LLM infrastructure
  • GPU clusters
  • model serving
  • training pipelines
  • inference
  • RAG
  • AI Agentic Systems