AI Security Engineer - Threat Modeling

Overview:

As an AI Security Engineer, you will be responsible for securing the end-to-end AI and machine learning lifecycle.

Your role is critical in protecting AI infrastructure, ensuring data privacy for training sets, and defending against adversarial ML threats.

You will work to integrate security into model development and deployment, providing expert guidance to help customers build and maintain resilient, secure AI environments on Google Cloud.

Responsibilities:

- Design and implement security controls for AI/ML pipelines, ensuring the integrity and confidentiality of models and data from ingestion to deployment.

- Conduct threat modeling and security assessments specifically targeting AI infrastructure, including model theft, data poisoning, and prompt injection attacks.

- Collaborate with data science and engineering teams to operationalize adversarial ML defenses and implement privacy-preserving techniques like differential privacy.

- Develop and deliver training on secure AI development practices and leading strategies for monitoring AI-specific security telemetry in Security Command Center Enterprise (SCCE).

Tech Stack:

- Google Cloud Security: Landing zone, NGFW, VPC-SC, SWP, VPN, KMS, SM.

- Infrastructure as Code: Terraform.

Minimum Qualifications:

- 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 platforms like Google Cloud (Vertex AI, GKE, etc.).

- 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 both technical and non-technical stakeholders.