This role focuses on leveraging AI and AI agents to enhance vulnerability detection and analysis within CrowdStrike's cybersecurity platform. The engineer will research, develop, and implement AI-driven solutions for identifying, validating, and prioritizing vulnerabilities, with a strong emphasis on automation and human oversight.

This role focuses on building and facilitating adoption of a modern Data+ML platform, modularizing ML code, establishing repeatable patterns for model development, deployment, and monitoring, and building a scalable platform for ML experimentation pipelines. It involves leveraging workflow orchestration tools, cloud services, and CI/CD frameworks, with a future focus on generative AI use cases. The role emphasizes production-focused ML engineering and bridging the gap between model development and operational success.

This role focuses on designing and implementing enterprise-wide intelligent automation initiatives using N8N, Tray.ai, and AI platforms like MCP and Gemini Enterprise. The engineer will develop AI-powered workflows, create system integrations, build monitoring systems, and implement agentic workflows for complex decision-making. The role requires strong technical expertise in automation and AI technologies to enhance operational efficiency.

This role focuses on building and scaling an ML Experimentation Platform, encompassing data preparation, feature engineering, model training, and model serving. It involves developing scalable ML pipelines, modularizing ML code, establishing patterns for model development and deployment, and leveraging workflow orchestration tools. The role also involves integrating with cloud services and CI/CD frameworks, with future plans for generative AI use cases.

Backend Engineer III for CrowdStrike's Falcon Exposure Management team, focusing on building a next-generation Cyber Asset Attack Surface Management (CAASM) platform. The role involves architecting and developing a cloud-native integration platform that connects with numerous security vendors and cloud providers, processes high volumes of events, and models diverse assets in real-time. Key aspects include building large-scale data pipelines, developing AI-powered integration tools with agentic workflows, implementing intelligent entity resolution using ML, and designing self-learning systems. The platform aims to create a unified view of organizational risk and serve as the central nervous system for enterprise security operations.