This role will drive the design and implementation of security capabilities protecting AI models, AI-powered applications, and AI developer platforms across Ford's enterprise ecosystem. The position will focus on securing both internally developed AI systems and third-party AI technologies, ensuring governance, runtime protection, and operational monitoring.

You will help build and operate a next-generation AI security platform that integrates capabilities from Microsoft AI Security, Palo Alto Prisma AIRS, Google Model Armor, and enterprise security platforms, enabling safe and scalable AI adoption across Ford.

Responsibilities

AI Security Platform Engineering

• Design and build scalable AI security platform capabilities protecting AI models, AI pipelines, and AI applications.
• Implement security across the AI lifecycle, including model governance, runtime protection, and secure AI deployment.
• Integrate enterprise AI protection capabilities including Microsoft AI Security, Prisma AIRS, and Google Model Armor.

AI Endpoint & Runtime Security

• Implement AI endpoint protection capabilities, including KOI AI endpoint security, to protect AI workloads running on enterprise endpoints and developer environments.
• Secure AI interactions across developer endpoints, APIs, and AI-enabled applications.
• Implement controls to mitigate prompt injection, data leakage, model abuse, and adversarial AI threats.

AI Threat Detection & Security Operations

• Partner with Cybersecurity Team & Integrate AI security telemetry with enterprise detection platforms such as Google SecOps.
• Support SOC to build detection capabilities for AI-specific threats and misuse patterns.

Cloud & Infrastructure Security

• Secure AI workloads across Google Cloud (GCP), and Microsoft Azure.
• Implement secure infrastructure using Terraform and Infrastructure-as-Code.
• Design security controls for Kubernetes-based AI platforms, APIs, and microservices.

Engineering & Automation

• Develop automation and security tooling using Python, APIs, and modern full-stack development practices.
• Build reusable security services and APIs supporting AI engineering teams.
• Enable DevSecOps automation across AI development pipelines.

Leadership & Collaboration

• Lead and mentor a team of AI security engineers and platform engineers.
• Partner with AI engineering, platform engineering, and cybersecurity teams to embed security into enterprise AI development.
• Define the AI security engineering roadmap, standards, and platform capabilities.

Qualifications

• 12+ years of experience in cybersecurity, cloud security, or platform engineering.
• 3+ years of experience securing AI/ML platforms or AI-driven applications.
• 4+ years of hands-on software development experience, preferably in Python.
• Strong expertise in:
• AI / ML security
• API and microservices security
• Full-stack development
• Hands-on experience with:
• Kubernetes security
• Terraform / Infrastructure-as-Code
• Cloud platforms (GCP, AWS, Azure)

Preferred Qualifications

• Experience implementing enterprise AI security platforms.
• Experience with AI protection technologies, including:
• Microsoft AI Security
• Palo Alto Prisma AIRS
• Google Model Armor
• KOI AI Endpoint Security
• Google Security Command Center Enterprise (SCCE)
• Experience securing LLM-based applications and generative AI systems.
• Familiarity with AI threat models, adversarial AI techniques, and AI governance frameworks.

Preferred Certifications

• CISSP – Certified Information Systems Security Professional
• CCSP – Certified Cloud Security Professional
• Google Professional Cloud Security Engineer
• AWS Security Specialty
• Microsoft Azure Security Engineer (AZ-500)
• Certified Kubernetes Security Specialist (CKS)

XML job scraping automation by YubHub