AI Security Engineer
Skills
Google Cloud PlatformPipelinesSecurity ControlsSecurity Information And Event ManagementMachine Learning SecurityAdversarial Machine LearningData Poisoning
What the job involves
The main requirements, responsibilities and hiring steps.
Requirements
- Strong background in security engineering with specialized experience in AI/ML security including model protection and adversarial machine learning
- Proven experience securing AI infrastructure and cloud-native services on Google Cloud such as Vertex AI and GKE
- Deep understanding of data privacy regulations and technical implementations for securing large-scale training datasets
- Ability to conduct technical security workshops and communicate complex AI risks to technical and non-technical stakeholders
- Experience with Google Cloud Professional Machine Learning Engineer or Professional Security Engineer certification preferred
- Experience with AI security frameworks and AI-specific red teaming or audits preferred
- Familiarity with securing Generative AI and Large Language Models in production environments preferred
Nice to have
- Collaborative
- Analytical
- Detail-oriented
- Security-minded
- Communication skills
Day to day
- Design and implement security controls for AI/ML pipelines to protect models and data from ingestion to deployment
- Conduct threat modeling and security assessments for AI infrastructure to identify model theft data poisoning and prompt injection risks
- Collaborate with data science and engineering teams to operationalize adversarial ML defenses and privacy-preserving techniques
- Develop and deliver training on secure AI development practices and monitor AI-specific security telemetry in SCCE