AI Security Engineering Manager
Tasks
- Build reusable security services and APIs
- Define AI security engineering roadmap and standards
- Design Kubernetes security controls
- Design and build AI security platform capabilities
- Detect AI threats and misuse
- Develop security automation and tooling
- Enable DevSecOps automation
- Implement AI endpoint and runtime security
- Implement AI lifecycle security
- Implement infrastructure as code with Terraform
- Integrate AI security telemetry with SOC platforms
- Integrate enterprise AI protection platforms
- Lead and mentor AI security engineers
- Mitigate prompt injection and data leakage
- Secure AI workloads on cloud platforms
Perks/Benefits
- N/A
Skills/Tech-stack
AI Security | AI/ML | AI/ML Security | API Security | AWS | Abuse prevention | Adversarial AI | Cloud Security | Cloud platform | Command Center | Data Leakage Prevention | Data leakage | DevSecOps | Full Stack | Full-Stack Development | Generative AI | Generative AI Security | Google Cloud | Google Cloud Platform | Google SecOps | Google Security Command Center | Google security | Infrastructure as Code | Kubernetes | Kubernetes security | LLM security | Leakage prevention | ML Security | Microservices Security | Microsoft Azure | Model Abuse Prevention | Model abuse | Prompt injection | Prompt injection mitigation | Python | Security Command Center | Security telemetry | SoC integration | Stack development | Terraform | “as-code”
Education
N/A
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