AI Security Engineer

Job Summary The AI Security Engineer will be a key member of the new AI initiative, responsible for designing, implementing, and maintaining security measures for their AI/ML platform on AWS. This role will focus on securing AI models, data pipelines, and infrastructure while contributing to the development of AI governance and security policies. The ideal candidate will have a strong background in cybersecurity, experience with AI/ML systems, and expertise in cloud security, particularly within AWS environments.

Key Responsibilities AI/ML Security Design and Implementation:

Develop and implement security controls for AI/ML models, including data protection, model integrity, and adversarial attack mitigation. Secure data pipelines and ensure compliance with data privacy regulations (e.g., GDPR, HIPAA). Integrate security best practices into AI/ML workflows, including secure model training and deployment.

Cloud Security:

Design and maintain secure AWS architectures for AI/ML workloads, leveraging services like AWS SageMaker, Lambda, and S3. Implement identity and access management (IAM) policies, encryption, and network security controls in AWS. Monitor and respond to security incidents within the AI/ML platform.

AI Governance and Policy Development:

Collaborate with the Director of Security and Compliance to develop AI governance frameworks, including ethical AI use and risk management. Create and enforce security policies for AI systems, ensuring alignment with industry standards and regulatory requirements. Conduct risk assessments and audits of AI/ML systems to identify and mitigate vulnerabilities.

Collaboration and Support:

Work closely with Data Engineers, ML Engineers, and DevOps Engineers to embed security into the AI/ML development lifecycle. Provide guidance and training to team members on AI security best practices. Support the Technical Project Manager in ensuring security milestones are met within project timelines.

Threat Monitoring and Incident Response:

Monitor AI/ML systems for potential threats, including adversarial AI attacks and data breaches. Develop and execute incident response plans specific to AI/ML environments. Stay updated on emerging AI security threats and incorporate proactive measures.

Required Qualifications Education:

Bachelor's degree in Computer Science, Cybersecurity, Information Technology, or a related field. Master's degree preferred.

Experience:

5+ years of experience in cybersecurity, with at least 2 years focused on securing AI/ML systems or cloud-based environments. Hands-on experience with AWS security tools and services (e.g., AWS IAM, KMS, CloudTrail, GuardDuty). Familiarity with AI/ML frameworks (e.g., TensorFlow, PyTorch) and their security implications. Experience developing or implementing security policies and governance frameworks. Knowledge of data privacy regulations (e.g., GDPR, HIPAA) and their application to AI systems. Certifications (Preferred): AWS Certified Security - Specialty Certified Ethical Hacker (CEH) Certified AI Practitioner (CertNexus) or equivalent AI-focused certification

Technical Skills:

Proficiency in securing cloud environments, particularly AWS. Experience with secure coding practices and vulnerability management. Knowledge of AI-specific security risks, such as model inversion, data poisoning, and adversarial attacks. Familiarity with Linux-based systems and scripting (e.g., Python, Bash). Understanding of DevOps practices and tools (e.g., Docker, Kubernetes, CI/CD pipelines). Preferred Qualifications Experience in the BioTech or healthcare industry, with knowledge of regulatory compliance (e.g., FDA, HIPAA). Familiarity with AI ethics and governance frameworks. Prior experience working on large-scale AI/ML projects in a cloud environment.