Manager at PwC contributing to digital transformation in Utilities through technology consulting and stakeholder management. Focused on creating strategies and providing technology solutions in a data - driven world.
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
Tech skills
Location requirements
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