AI Risk Engineer

Details

• Define and implement security controls specifically targeting LLM and AI-powered application risks.
• Build threat models for AI systems, including prompt injection, jailbreaks, data exfiltration, and abuse patterns.
• Design and deploy guardrails, content filters, and policy enforcement layers around model endpoints.
• Implement runtime detection and response capabilities for adversarial prompts and abusive behavior.
• Secure training and fine-tuning pipelines, including data provenance, integrity, and access controls.
• Design controls for sensitive data handling, retention, and redaction in LLM workflows.
• Lead red-team exercises against AI systems and drive remediation of identified weaknesses.
• Evaluate and harden third-party AI services and open-source AI components used internally.
• Implement identity, authorization, and tenant-isolation patterns for multi-tenant AI services.
• Drive supply chain security for ML artifacts including weights, datasets, and inference dependencies.
• Collaborate with privacy, legal, and compliance teams to ensure AI systems meet regulatory obligations.
• Develop monitoring, logging, and detection strategies tailored to AI workloads.
• Lead incident response for AI-specific security events and drive durable improvements.
• Stay current with adversarial ML, LLM security research, and emerging regulatory developments.
• Bachelor’s or Master’s degree in Computer Science, Cybersecurity, or a related discipline.
• Six or more years of security engineering experience, including significant work on AI or ML systems.
• Strong understanding of LLM internals, modern AI architectures, and common failure modes.
• Hands-on experience designing security controls for AI-powered applications.
• Deep knowledge of application security, identity, and cryptography fundamentals.
• Experience with threat modeling and security architecture review processes.
• Familiarity with adversarial ML, prompt injection, and model abuse research.
• Proficiency in Python and at least one systems language.
• Strong understanding of cloud security and modern infrastructure controls.
• Excellent written and verbal communication skills.
• Publications, talks, or CTF participation in AI security topics.
• Experience with red-teaming LLM-based products.
• Familiarity with privacy-preserving ML techniques such as differential privacy.
• Exposure to regulated industries with strict data handling requirements.
• Open-source contributions to AI security tooling.