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AI Security Engineer
Skills
Artificial IntelligenceGenerative AIGoogle Cloud PlatformLarge Language ModelsMachine LearningSecurity Information And Event ManagementVertex
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 including 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
Nice to have
- Google Cloud certification
- AI security framework experience
- AI red teaming experience
- LLM security experience
Day to day
- Secure the end-to-end AI and machine learning lifecycle across ingestion training and deployment.
- Design and implement security controls for AI and ML pipelines to protect model and data integrity and confidentiality.
- Conduct threat modeling and security assessments for AI infrastructure including model theft data poisoning and prompt injection attacks.
- Collaborate with data science and engineering teams to operationalize adversarial ML defenses privacy-preserving techniques and secure AI telemetry monitoring.