Senior Cybersecurity Platform Engineer

We are the movers of the world and the makers of the future. We get up every day, roll up our sleeves and build a better world -- together. At Ford, we’re all a part of something bigger than ourselves. Are you ready to change the way the world moves?

Enterprise Technology plays a critical part in shaping the future of mobility. If you’re looking for the chance to leverage advanced technology to redefine the transportation landscape, enhance the customer experience and improve people’s lives, this is the opportunity for you. Join us and challenge your IT expertise and analytical skills to help create vehicles that are as smart as you are.

This role is responsible for designing, implementing, automating, and maintaining security platforms that support enterprise cybersecurity operations. The role focuses on integrating security agents/tools, improving detection capabilities, ensuring platform reliability, and enabling security teams through scalable infrastructure and automation. This role demands cloud experience blended with platform engineering capabilities to mature the AI Security platform/tools.

What you'll do...

• Design, implement, maintain, and improve security platforms and tools that protect the organization’s IT infrastructure.

• Optimize security technologies to detect, prevent, and respond to security threats in real time.

• Collaborate with engineering, IT, and security operations teams to deploy and support enterprise cybersecurity platforms and solutions.

• Secure in-house and public AI and ML/DL systems against cyber threats, adversarial attacks, and data breaches across the solution lifecycle.

• Design and implement robust security platforms supporting enterprise security needs (e.g., unified telemetry pipeline like BindPlane, SIEM like QRadar, SecOps, and AI security).

• Define and maintain guidelines and controls to secure AI systems, including data protection, model security, and compliance requirements.

• Apply established frameworks as references/baselines (e.g., Google Secure AI Framework (SAIF), NIST AI Risk Management Framework, Framework for AI Cybersecurity Practices (FAICP)).

• Identify, assess, and mitigate AI-specific security risks (adversarial attacks, data poisoning, model inversion, unauthorized access).

• Conduct vulnerability assessments and penetration testing on AI models and data pipelines.

• Ensure AI data is encrypted, anonymized, and securely stored.

• Implement access controls for sensitive AI data and models (RBAC, ABAC, Zero Trust).

• Protect AI models from tampering, theft, and adversarial manipulation during training and deployment.