Senior AI Infrastructure Engineer, LLM/AI Platforms

As a global leader in cybersecurity, CrowdStrike protects the people, processes and technologies that drive modern organizations. Since 2011, our mission hasn t changed - we re here to stop breaches, and we ve redefined modern security with the world s most advanced AI-native platform. We work on large scale distributed systems, processing almost 3 trillion events per day and this traffic is growing daily. Our customers span all industries, and they count on CrowdStrike to keep their businesses running, their communities safe and their lives moving forward. We're proud to work for a mission-driven company leveraging AI to transform the way we work. CrowdStrikers drive their careers through flexibility and autonomy while also being expected to contribute to a culture of responsible AI adoption, experimentation, and innovation. We use an AI-first mindset as a force multiplier to proactively and continuously accelerate execution, build expertise, uncover insights, and solve complex problems. We re always looking to add talented CrowdStrikers to the team who have limitless passion, a relentless focus on innovation and a fanatical commitment to our customers, our community and each other. Ready to join a mission that matters? The future of cybersecurity starts with you. About the Role: CrowdStrike is looking for a Senior AI Infrastructure Engineer with expertise in Large Language Models (LLMs) Infrastructure and data platforms to join our growing AI Infrastructure Team. You will be a key leader, helping to design, build, and deploy cutting-edge AI infrastructure that powers our next generation of AI-driven security products. This role requires hands-on experience in LLM infrastructure to support multiple large scale training pipelines and scalable AI-powered systems. You will champion engineering best practices, write high-quality code, and actively mentor and strengthen the team s technical knowledge and capabilities. CrowdStrike is a computer security company, but we do not require candidates for this role to have prior security industry experience. We will mentor and train in security topics as needed. We do expect a strong interest in CrowdStrike's mission and a willingness to engage with the needs of our product teams. The scale of our systems and data are approaching Exabytes in size. Experience with extremely large-scale systems, including DevSecOps patterns, practices, and standards are important for this work. What You'll Do: • Provision and configure large GPU clusters and compute resources for LLM training, finetuning, and inference workloads. • Develop and optimize LLM model-serving infrastructure, including deployment and optimization of various inference frameworks. • Lead model lifecycle management including versioning, checkpointing and reproducibility across training and inference deployments. • Design and champion robust evaluation frameworks to assess model performance, accuracy, and reliability, ensuring AI systems are consistently at production-ready standards. • Identify and address GPU utilization and GPU memory efficiency bottlenecks and apply techniques like quantization, batching, and caching. • Architect and maintain data platforms and pipelines specifically designed to support LLMs, Retrieval-Augmented Generation (RAG), and AI Agentic Systems at scale. • Deliver production-ready code with a focus on performance, maintainability, and testing rigor, ensuring the ability to ship fast without compromising quality. • Apply expertise in data modeling, normalization, and semantic cataloging for AI/ML workloads. • Define and enforce best practices for MLOps/DataOps surrounding LLMs, including monitoring, observability, and zero-touch recovery mechanisms for AI services. • Document architectural designs thoroughly and communicate technical decisions clearly to stakeholders • Collaborate across the organization with Data Scientists, Product Managers, and other engineering teams to transform research prototypes into robust, production-grade services. Tech Stack (Experience in several areas is expected): • Hands-on experience with MLOps Tools (MLflow, Sagemaker, Vertex AI). • Strong understanding of CUDA, NVIDIA drivers, GPU, and TPU compute fundamentals. • Experience with inference serving frameworks such as vLLM and Triton Inference Server. • Proficiency with distributed training frameworks including Pytorch, Ray, Megatron, and JAX. • Expert-level proficiency in a high-level coding language (Python). • Deep knowledge of containerization and orchestration (Docker, Kubernetes, Slurm, Airflow). • Proficiency with Infrastructure as Code tooling like Terraform and Ansible. • Experience with cloud platforms (AWS, GCP, or OCI) and related data services. What You'll Need: • Bachelor s degree in Computer Science, Data Engineering, or a related STEM field; Master s degree preferred • 6+ years of experience in Infrastructure/Data Engineering, with at least 2 years focused on bu...