Principal Data Engineer, LLM/AI Platforms (Remote)

CrowdStrike is a global cybersecurity company that uses an AI-native platform to protect organizations and stop breaches. The Principal Data Engineer will lead the design, implementation, and deployment of large-scale data infrastructure for LLMs, RAG, and AI agentic systems, while mentoring engineers and driving engineering excellence.

Responsibilities:

• Architect, implement, and optimize data platforms and pipelines specifically designed to support LLMs, Retrieval-Augmented Generation (RAG), and sophisticated AI agentic systems at Exabyte scale
• Drive the adoption and deployment of agentic workflows and agent harnessing techniques to create autonomous, data-driven security features
• Design and implement highly scalable, fault-tolerant, and cost-effective data solutions, emphasizing rapid iteration and high-quality deployment
• Write elegant, production-ready code with a focus on performance, maintainability, and testing rigor, ensuring the ability to ship fast without compromising quality
• Provide technical leadership and deep expertise in data modeling, normalization, and semantic cataloging for AI/ML workloads
• Establish best practices for MLOps/DataOps surrounding LLMs, including monitoring, observability, and zero-touch recovery mechanisms for AI services
• Actively mentor engineers, conducting technical workshops, leading design reviews, and strengthening the team's knowledge in cutting-edge AI platform technologies
• Collaborate across the organization with Data Scientists, Product Managers, and other engineering teams to transform research prototypes into robust, production-grade services
• Own the end-to-end lifecycle of critical data services: development, testing, deployment, and monitoring

Requirements:

• Master's degree or PhD in Computer Science, Data Engineering, or a related STEM field, or equivalent practical experience
• 10+ years of progressive experience in Data Engineering/Platform Engineering, with at least 3 years focused on architecting and building platforms for AI/ML or Data Science at massive scale
• Demonstrable hands-on experience in LLM engineering (fine-tuning, prompt engineering, deployment), RAG, and developing agentic workflows
• Proven track record of designing and delivering large-scale distributed systems (sharding, partitioning, concurrency)
• Exceptional ability to write clean, elegant, performant, and well-tested code, coupled with a proactive mindset for delivering results quickly
• A thorough understanding of engineering practices, including effective peer code reviews, resilient architecture design, and comprehensive testing paradigms
• Prior experience in a Principal or Staff level engineering role, demonstrating technical leadership and mentorship capabilities
• Proven experience utilizing AI technologies to enhance decision-making, streamline workflows and processes, improve efficiency and drive business outcomes
• Expertise in several key areas
• MLOps Tools (MLflow, Sagemaker, Vertex AI)
• Experience with common agentic workflow frameworks (e.g., LangChain, LlamaIndex)
• Expert-level proficiency in a high-level coding language (Python, or JVM technologies)
• Deep experience with distributed data processing frameworks (e.g., Spark, Dask, Flink)
• Strong expertise with cloud platforms (AWS, GCP, or OCI) and related data services
• Containerization and orchestration mastery (Docker, Kubernetes)
• Message queuing and streaming technologies (Kafka, Pulsar)
• Data Warehousing (Snowflake, BigQuery) and Data Orchestration (Airflow, Kubeflow)
• Direct experience building, deploying, and managing LLMs in a production environment
• Prior experience in the cybersecurity, intelligence, or high-compliance industries
• Contributions to open-source projects related to data or AI/ML