Senior AI Security Engineer

About the job

Job Description As a Senior AI Security Engineer, you will be crucial in safeguarding our advanced AI models, data, and infrastructure. You'll work closely with Data Scientists, Data Engineers, and MLOps / DevOps teams. Additional responsibilities include: • Implement defenses against AI-specific attacks (adversarial, prompt injection, data leakage) • Conduct AI-focused security assessments, penetration tests, red / purple team exercises • Analyze AI system vulnerabilities, develop mitigation strategies, and create AI risk heat maps • Implement security controls throughout the AI / ML lifecycle (data handling, training with GPU isolation, deployment, monitoring, versioning, provenance). Integrate SAST / DAST for ML artifacts • Manage audit trails and automated compliance checks • Implement AI-specific incident response and develop regulatory disclosure playbooks • Manage AI security monitoring, implement executive dashboards linking security to business KPIs, develop security metrics (Adversarial Risk Score, Model Drift Index) • Implement secure training environments and fine-grained data access controls • Contribute to AI-generated fraud detection in transaction monitoring systems. • Act as an AI security SME, continuously research emerging threats Qualifications Required: • Bachelor's degree in computer science, Engineering, or a related field. • 3-5+ years of experience in cybersecurity (application, cloud and data security) with strong proficiency in security scripting, automation, and tool development. • Deep understanding of AI-specific threat vectors (adversarial attacks, prompt injection, data leakage). • Demonstrated, hands-on experience with the Azure or AWS Cloud ecosystem and its security services. • Proven experience translating regulatory frameworks (NIST AI RMF, EU AI Act) into technical controls. Preferred: • Knowledge of AI security frameworks. • Azure Cloud security services ecosystem (Microsoft Sentinel, Azure Monitor, Azure Policy, Purview, Key Vault, Azure ML security). • Securing MLOps / LLMOps pipelines (data versioning, provenance, GPU isolation). • Security frameworks (OWASP AI Security & Privacy Guide). • Automated compliance checks (e.g., via Azure Monitor). • Security monitoring (e.g., Microsoft Sentinel with KQL). • Secure training environments (Azure ML, HSMs). • Data access controls (Azure Policy, Purview). • Security assessment tools (SAST, DAST) adapted for ML. • Familiarity with relevant data privacy and security regulations (GDPR, DORA).

Qualifications

• Bachelor's degree in computer science, Engineering, or a related field.
• 3-5+ years of experience in cybersecurity