About this role

As the Senior Manager of AI Security Engineering, you will own the technical strategy, architecture, development, and operational delivery of Ford’s AI security platform. You will lead a team of engineers while remaining actively involved in system design, code reviews, automation, and technical decision-making. You will partner with AI, platform, and cybersecurity teams to build security capabilities that protect AI applications throughout the lifecycle, including data ingestion, model development, inference, deployment, and monitoring. Success in this role requires a builder mindset, strong product ownership, and the ability to develop scalable security controls through automation rather than manual processes.

Required Qualifications

- Bachelor's degree in Computer Science, Cybersecurity, Engineering, or a related field, or equivalent experience.
- 10+ years of experience in software engineering, cybersecurity, platform engineering, or related disciplines.
- 3+ years of experience securing AI/ML, generative AI, or data-driven platforms.
- Experience leading engineering teams and delivering enterprise-scale technology solutions.

Technical Skills

Software Engineering

- Strong hands-on development experience with Python and at least one modern backend technology (Node.js, Java, or Go).
- Experience building RESTful APIs and microservices.
- Proficiency with modern frontend frameworks such as React, Angular, or Vue.
- Strong understanding of secure application development practices.

Cloud & Platform Engineering

- Experience with Azure, GCP, or AWS.
- Strong understanding of Kubernetes, container security, and cloud-native architectures.
- Experience with Infrastructure as Code, preferably Terraform.
- Familiarity with CI/CD pipelines and Git-based development workflows.

Security Engineering

- Deep knowledge of application security principles and OWASP standards.
- Experience with API security, Identity and Access Management (IAM), encryption, and secrets management.
- Strong understanding of threat modeling, security monitoring, and incident response.

AI Security

- Knowledge of generative AI and large language model (LLM) security risks.
- Experience with prompt injection mitigation, data protection, model governance, and AI threat modeling.
- Familiarity with AI security frameworks, controls, and secure AI deployment practices.

Preferred Qualifications

- Experience building security platforms, developer tools, or enterprise security products.
- Experience securing LLM-based applications and AI platforms.
- Familiarity with Microsoft AI security solutions, Google Model Armor, Palo Alto Prisma AIRS, or similar technologies.
- Experience implementing AI security monitoring, governance, and risk management capabilities.
- Professional security or cloud certifications (CISSP, CCSP, AWS Security, Azure Security, GCP Security, or equivalent).

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