AI Security Engineer

Tasks

• Conduct AI threat modeling
• Conduct security reviews for AI initiatives
• Define AI control frameworks
• Define AI risk classification
• Define secure by design patterns
• Design AI security architecture
• Enforce security standards
• Ensure GDPR compliance
• Ensure data privacy compliance
• Identify AI-specific threats
• Implement AI security controls
• Implement content filters
• Implement output validation
• Integrate AI security checks into CI CD
• Mitigate adversarial attacks
• Mitigate model poisoning
• Mitigate prompt injection
• Monitor anomalous AI behavior
• Perform AI risk assessments
• Perform ML code security reviews
• Prevent training data leakage
• Secure AI APIs
• Secure LLM integrations
• Secure RAG pipelines
• Secure artifact storage
• Secure model registries
• Support AI red teaming

Perks/Benefits

• Employee referral program
• Generous time off
• Health insurance
• Pension benefits
• Remote work options
• Volunteer days
• Work-life balance
• Workation days

Skills/Tech-stack

API Security | AWS | Adversarial Attacks | Amazon SageMaker | Anomaly Detection | Azure | CI/CD | Cloud Security | Cloud platform | Content Filtering | Data leakage | Docker | Google Cloud | Google Cloud Platform | Kubeflow | Kubernetes | Language Models | Large Language Models | MLflow | Machine Learning | Model Serialization | Model poisoning | NLP | OWASP Top | OWASP Top 10 | Output Validation | Prompt injection | Python | RAG | Risk Assessment | Secure SDLC | Threat modeling | Top 10 | Vector Databases