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As a global leader in cybersecurity, CrowdStrike protects the people, processes and technologies that drive modern organizations. Since 2011, our mission hasnt changed - were here to stop breaches, and weve redefined modern security with the worlds most advanced AI-native platform. Our customers span all industries, and they count on CrowdStrike to keep their businesses running, their communities safe and their lives moving forward. Were also a mission-driven company. We cultivate a culture that gives every CrowdStriker both the flexibility and autonomy to own their careers. Were always looking to add talented CrowdStrikers to the team who have limitless passion, a relentless focus on innovation and a fanatical commitment to our customers, our community and each other. Ready to join a mission that matters? The future of cybersecurity starts with you.

What You'll Do:
- Act as the product leader for major initiatives around Cloud Detection & Response.
- Understands the Cloud Security markets and capabilities
- Utilize strategic insight and organizational skills to identify unmet customer needs, define use cases, and advance the functional capabilities of this offering
- Interact with different audiences such as Cloud Security Architects, DevOps, Incident Responders, Developers and Hunters to define innovative products.
- Collaborate closely with Engineering and Data Science to understand threat and attack trends and deliver new visibility and defenses
- Partner with marketing to help define go-to-market strategy & messaging, assist with product positioning, benefits, and target personas
- Engage with sales and support to champion recent releases and guide future product direction
- Partner with UX Professionals to help deliver a world class user experience
- Conduct competitive analyses and maintain current information regarding competitive activity
- Work on a team of high-performing product professionals in a very collaborative environment

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