AI Security Engineer

Be a key player at the forefront of the digital assets movement, propelling your career to new heights! Join a dynamic and rapidly expanding company that values and rewards talent, initiative, and creativity. Work alongside one of the most brilliant teams in the industry.

• 3–5+ years in software engineering, ML engineering, or application security
• Hands-on experience with AI/ML systems — LLMs, NLP models, or similar
• Python proficiency for automation and scripting
• Experience working with Claude Code
• Strong understanding of cloud platforms: AWS, Azure, or GCP
• Experience with API security, Docker, Kubernetes
• Knowledge of AI-specific security risks and mitigations
• Experience conducting threat modeling and risk assessments

Preferred Qualifications:

• Familiarity with RAG architectures, vector databases, ML pipelines (MLflow, Kubeflow, SageMaker)
• Experience in fintech or regulated environments
• Knowledge of AI governance frameworks (EU AI Act, NIST AI RMF, ISO/IEC 42001)
• Experience with AI red teaming
• Background in cybersecurity or application security (OWASP, Secure SDLC)

AI/ML Security Architecture:

• Design and implement security controls for AI/ML systems across development, training, and production.
• Secure LLM integrations, RAG pipelines, and AI APIs.
• Conduct threat modeling for AI systems and data pipelines.
• Define secure-by-design patterns for AI-powered features.

AI Threat Detection & Mitigation:

• Identify and mitigate AI-specific threats: prompt injection and jailbreak techniques, model poisoning and data contamination, adversarial attacks, training data leakage, insecure model serialization, excessive permissions in AI agents.
• Develop guardrails, content filters, and output validation mechanisms.
• Implement monitoring for anomalous AI behavior.

Secure Development & DevSecOps:

• Integrate AI security checks into CI/CD pipelines.
• Perform security reviews of ML code and AI-related infrastructure.
• Secure model registries and artifact storage.
• Collaborate with other engineers and platform teams to enforce security standards.

Data Protection & Compliance:

• Ensure AI systems comply with GDPR and data privacy regulations, financial industry regulatory requirements.
• Implement controls for sensitive data used in training and inference.
• Perform AI risk assessments aligned with internal risk methodology.

Governance & Policy:

• Contribute to AI security standards and internal policies.
• Define AI risk classification and control frameworks.
• Support security reviews for new AI initiatives / tools.