Site Security Monitor Associate ensures that SCIF being built meets client’s standards and Construction Security Plan. Responsible for implementing aspects of the Construction Security Plan at project site.
About the role
- Senior MLOps Engineer establishing and leading CrowdStrike's MLOps function. Shaping AI/ML-driven security solutions at scale for thousands of organizations worldwide.
Responsibilities
- 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
Requirements
- 6+ years of experience in MLOps, ML engineering, DevOps, or related infrastructure roles with focus on machine learning systems
- Proven track record of building and operating ML systems at scale in production environments
- Deep knowledge of cloud platforms (AWS, Azure, or GCP), containerization (Docker, Kubernetes), and infrastructure-as-code (Terraform, CloudFormation)
- Hands-on experience with ML workflow orchestration tools (e.g., Airflow, Kubeflow, MLflow, Metaflow) and building end-to-end ML pipelines
- Strong coding skills in Python; experience with additional languages is a plus
- Expertise in building automated deployment pipelines, version control, and modern DevOps methodologies
- Ability to think architecturally about long-term solutions while rolling up your sleeves to implement them
- Excellent communication skills and ability to work effectively with data scientists, engineers, and stakeholders with varying technical backgrounds
- Comfort with ambiguity and ability to build from scratch in a fast-paced environment
Benefits
- Market leader in compensation and equity awards
- Comprehensive physical and mental wellness programs
- Competitive vacation and holidays for recharge
- Paid parental and adoption leaves
- Professional development opportunities for all employees regardless of level or role
- Employee Networks, geographic neighborhood groups, and volunteer opportunities to build connections
- Vibrant office culture with world class amenities
- Great Place to Work Certified™ across the globe
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
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