Senior Cybersecurity Platform Engineer

Ford Ford · Auto · United States · Enterprise Product Line Management

Senior Cybersecurity Platform Engineer responsible for designing, implementing, and maintaining security platforms for enterprise cybersecurity operations, with a specific focus on securing AI/ML systems against cyber threats, adversarial attacks, and data breaches. This includes defining security guidelines, assessing risks, conducting vulnerability assessments, ensuring data and model protection, and implementing access controls for AI systems.

What you'd actually do

  1. Design, implement, maintain, and improve security platforms and tools that protect the organization’s IT infrastructure.
  2. Secure in-house and public AI and ML/DL systems against cyber threats, adversarial attacks, and data breaches across the solution lifecycle.
  3. Design and implement robust security platforms supporting enterprise security needs (e.g., unified telemetry pipeline like BindPlane, SIEM like QRadar, SecOps, and AI security).
  4. Define and maintain guidelines and controls to secure AI systems, including data protection, model security, and compliance requirements.
  5. Identify, assess, and mitigate AI-specific security risks (adversarial attacks, data poisoning, model inversion, unauthorized access).

Skills

Required

  • 5+ years of experience in security engineering, platform engineering, and AI/ML
  • Managing security platforms and tools in enterprise environments
  • Telemetry pipeline platforms (e.g., BindPlane), SIEM (e.g., Splunk, QRadar), and vulnerability management tools
  • Scripting and automation (Python, PowerShell, and/or Bash)
  • Infrastructure as Code (Terraform, Ansible)
  • Cloud security tools and platforms (GCP, AWS, Azure)
  • Container security (Docker, Kubernetes)
  • Networking protocols, firewalls, and network security best practices
  • AI/ML concepts, architectures, and AI security challenges
  • AI threat areas (adversarial attacks, data poisoning, model inversion, unauthorized access)
  • Vulnerability assessment and penetration testing on AI models and data pipelines
  • Data protection techniques (encryption, anonymization, secure storage) and secure access management (RBAC, ABAC, Zero Trust)
  • Incident response, monitoring tools, and threat intelligence platforms
  • Security frameworks and compliance references (SAIF, NIST, FAICP)
  • ITSM processes and tools (ServiceNow) and delivery practices/tools (Agile, JIRA)

Nice to have

  • Master’s degree in Computer Science, Information Security, or related field.
  • Understanding of cloud AI/ML services and deployment pipelines.
  • CISSP (Certified Information Systems Security Professional).
  • CCSP (Certified Cloud Security Professional).
  • Preferred certifications such as CAISF, AICERTs, AI for Cybersecurity Specialization, or equivalent.
  • GCP cloud certification or equivalent in AWS or Azure (preferred).
  • Additional cybersecurity certificates (preferred).

What the JD emphasized

  • AI/ML concepts, architectures, and AI security challenges
  • AI threat areas (adversarial attacks, data poisoning, model inversion, unauthorized access)
  • Vulnerability assessment and penetration testing on AI models and data pipelines
  • Data protection techniques (encryption, anonymization, secure storage) and secure access management (RBAC, ABAC, Zero Trust)
  • Security frameworks and compliance references (SAIF, NIST, FAICP)

Other signals

  • Securing AI/ML systems
  • AI security risks
  • AI data protection
  • AI model security