Note: The job is a remote job and is open to candidates in USA. CrowdStrike is a global leader in cybersecurity, dedicated to stopping breaches with advanced AI-native platforms. They are seeking a Senior AI Infrastructure Engineer to design, build, and deploy AI infrastructure for next-generation security products, focusing on Large Language Models (LLMs) and scalable AI systems.
Responsibilities
- 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
Skills
- 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 building and maintaining platforms/pipelines that support LLM-based systems and applications
- Demonstrable hands-on experience in LLM infrastructure engineering including cluster provisioning, optimizing training workloads, and maintaining inference pipelines
- Exceptional ability to write clean, elegant, performant, and well-tested code, coupled with a strong focus on action and delivering results quickly
- Thorough understanding of engineering practices including effective peer code reviews and resilient architecture design
- Demonstrates technical leadership and mentorship capabilities
- Proven experience utilizing AI technologies to enhance decision-making, streamline workflows and processes, improve efficiency and drive business outcomes
- 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
- Prior experience in the cybersecurity, intelligence, or high-compliance industries
- Direct experience building, deploying, and managing LLMs in a production environment
- Experience with common agentic workflow frameworks (e.g., LangChain, LlamaIndex)
- Experience with distributed data processing frameworks (e.g., Spark, Dask, Flink)
Benefits
- Eligibility for bonuses
- Equity grants
- A comprehensive benefits package that includes health insurance, 401k and paid time off
- Market leader in compensation and equity awards
- Comprehensive physical and mental wellness programs
- Competitive vacation and holidays for recharge
- Paid parental and adoption leaves
- Professional development opportunities for all employees regardless of level or role
- Employee Networks, geographic neighborhood groups, and volunteer opportunities to build connections
- Vibrant office culture with world class amenities
- Great Place to Work Certified™ across the globe
Company Overview