CrowdStrike's Data Science Studio is seeking a pioneering Senior MLOps Engineer to establish and lead our MLOps function from the ground up. As the first MLOps engineer in the studio, you will play a foundational role in shaping how we build, deploy, and scale machine learning systems that protect thousands of organizations worldwide.

This is a unique opportunity to define the technical strategy, influence the technology stack, and architect the infrastructure that will power our AI/ML-driven security solutions for years to come.

This role combines strategic vision with hands-on execution. You'll work at the intersection of data science, engineering, and production operations - building production-grade systems that operate at immense scale while collaborating closely with highly technical data scientists and ML engineering teams across CrowdStrike.

What You'll Do:
- Architect MLOps infrastructure from the ground up: Design and implement the foundational MLOps platform, establishing best practices, tooling, and workflows that will scale with our growing data science initiatives
- Define technology strategy: Evaluate, select, and integrate MLOps technologies and platforms that best serve our needs - from experiment tracking and model versioning to deployment pipelines and monitoring systems
- Build production-grade ML pipelines: Develop robust, scalable pipelines for model training, validation, deployment, and monitoring that handle massive data volumes and ensure reliability in production
- Enable data scientist productivity: Create tools, frameworks, and automation that empower data scientists to move quickly from research to production while maintaining high quality and reliability standards
- Establish monitoring and observability: Implement comprehensive monitoring, logging, and alerting systems to ensure ML models perform optimally in production and issues are detected proactively
- Drive MLOps culture and practices: Champion best practices in ML engineering, CI/CD for ML, model governance, and reproducibility across the data science organization
- Collaborate cross-functionally: Partner closely with data scientists to understand their workflows and pain points, and work with ML engineering teams to ensure seamless integration with broader platform capabilities
 -Scale for the future: Design systems with scalability, security, and maintainability in mind, anticipating the needs of a rapidly growing ML portfolio