AI Security Engineer

We are seeking a skilled and security-minded AI Security Engineer to join our team. In this role, you will be responsible for identifying and mitigating security risks in artificial intelligence systems, ensuring the confidentiality, integrity, and availability of AI models and data. You will work cross-functionally with data scientists, engineers, and cybersecurity teams to design, build, and maintain secure AI systems across their entire lifecycle.

Key Responsibilities

• AI Threat and Risk Assessment: Identify and assess AI-specific threats, including adversarial attacks, data poisoning, model inversion, and data leakage.
• Secure AI Pipeline Development: Implement security best practices throughout the AI/ML development lifecycle—from data ingestion and training to deployment and monitoring.
• Tooling & Automation: Develop and deploy tools for automated threat detection, model monitoring, and vulnerability scanning in AI workflows.
• Cross-functional Collaboration: Partner with software engineers, data scientists, DevOps, legal, and compliance teams to ensure secure and responsible AI development.
• Monitoring & Incident Response: Establish real-time monitoring, logging, and incident response procedures for AI systems in production.
• Compliance & Governance Alignment: Ensure alignment with relevant cybersecurity standards and AI-related regulations (e.g., ISO/IEC 27001, NIST, GDPR).
• Utilizes AI-enabled tools (e.g., chatbots, document automation, analytics assistants) to improve efficiency, accuracy, and streamline routine tasks while following company AI governance and data privacy standards
  
  
  

• Proficiency in Python; experience with AI/ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Solid understanding of cybersecurity fundamentals and secure software development practices.
• Experience with cloud platforms (AWS, Azure, or GCP) and securing cloud-native applications.
• Familiarity with security tools (e.g., static/dynamic analyzers, monitoring tools, threat modeling platforms).
• Experience building or securing large-scale ML infrastructure.
• Knowledge of privacy-preserving ML techniques (e.g., differential privacy, federated learning).
• Familiarity with AI governance, fairness, or explainability frameworks.
• Skills in prompting AI systems and assessing output quality
• Ability to leverage AI to ideate, develop, and scale to the needs of their department
  
  
  

• 2–4 years of professional experience in cybersecurity, software engineering, or AI/ML system development
• AI certificate or Diploma can offset 1 year of work experience
• 1+ years working directly on securing AI/ML systems or data pipelines