AI Security Engineer

Overview: AI Security Engineer

Secure AI/ML pipelines, Generative AI apps, and agentic AI systems on GCP; blends traditional GCP security with AI-specific threats. 5070% AI security focus.

Primary Skills

1. AI/ML Pipeline Security & Adversarial ML Defense (model theft, prompt injection, data poisoning)

2. Generative AI & Agentic Security (Gemini Enterprise Security, Model Armor)

3. GCP AI Infrastructure Security (Vertex AI, GKE)

Secondary Skills

1. Python & Secure Code Deployment

2. DLP / Sensitive Data Protection (data pipeline integration)

Key Qualifications:

- Secure AI/ML pipelines against adversarial threats

- Implement Model Armor & Gemini Enterprise Security

- Secure agentic AI workflows & enterprise data access

- Harden Vertex AI & GKE for AI workloads

- Integrate DLP/SDP into training/inference pipelines

- Build Python-based security tooling

- Partner with ML teams for security-by-design

- Track OWASP LLM Top 10, MITRE ATLAS

Minimum Qualifications:

- 5–8 years in security engineering, ~3 years in AI/ML security or applied ML

- Hands-on with Vertex AI, GKE, Model Armor/Gemini

- Strong Python; familiarity with TensorFlow/PyTorch

- Understanding of adversarial ML techniques

Preferred Qualification:

- GCP certification (Security Engineer or ML Engineer) preferred