• Parse and analyze large volumes of transactional and behavioral data to identify indicators of fraud and risk • Develop and maintain a rules-based risk engine that adapts to emerging threat patterns • Conduct ad hoc investigations on suspicious activity and emerging risk scenarios • Serve as the subject matter expert on fraud patterns, adversarial tactics, and risk mitigation strategies • Design, build, and maintain Snowflake tables, views, and data pipelines to support risk detection workflows • Write SQL queries to extract, transform, and analyze risk-related data at scale • Create and optimize data models that enable real-time and batch risk scoring • Develop Python scripts to automate data collection, analysis, and reporting processes • Leverage AI tools (including LLMs) to enhance fraud detection capabilities and generate risk insights • Collaborate with data science teams to implement machine learning models for anomaly detection • Use AI-assisted analysis to identify patterns in unstructured data (emails, user behavior, device fingerprints) • Continuously refine AI-powered detection rules based on feedback and emerging threats • Effectively communicate risk findings to technical and non-technical audiences across the organization • Document risk assessments, investigation findings, and rule changes in a clear, actionable manner • Work collaboratively with eCommerce, Sales, Legal, and Security teams to improve CrowdStrike's overall risk posture • Monitor the risk engine and Slack channels for real-time alerts and triage incidents as they unfold • Diagnose and respond to risk engine failures, false positives, and system anomalies • Review daily risk metrics and dashboards to identify trends and areas for improvement • Proactively identify vulnerabilities in the customer onboarding process and recommend mitigations

• Provide day-to-day leadership of AI and automation strategy, planning, and execution • Serve as a strategic thought partner to the CTO and ELT • Establish a clear AI roadmap and execution cadence • Build, lead, and develop a high-performing cross-functional AI organization • Oversee and enhance communication of all AI and automation reporting • Own the design and governance of AI/automation platforms • Develop and maintain key AI performance indicators (KPIs) • Partner with product, operations, and client-facing teams • Champion AI adoption and literacy across the organization • Participate as a core leader within the technology and executive management teams

• Develop MLIR-based graph representations and optimizations for future GPU architectures • Partner with framework and hardware teams to enable new model patterns and upcoming GPU architectural features • Define APIs and MLIR dialects, conduct performance optimizations and analysis, implement compiler optimizations and kernel generation for neural networks, and contribute to other general software engineering work

• Build and run AI agent workflows involving LLMs and tools to automate multi-step processes • Build orchestration services that coordinate model calls, tools, and human review • Develop APIs that expose AI agents to internal apps and UIs or equivalent experience • Connect existing ETL and data pipelines as tools and context for agents • Construct simple web or chat interfaces for workflows powered by artificial intelligence • Ensure scalability, performance, and security of AI services and data flows.