Leads the strategy, architecture, development, and operations of Ford’s AI security platform. Manages AI security engineers while contributing to system design, coding, automation, and technical decisions. Builds full-stack, cloud-native security services, APIs, dashboards, DevSecOps controls, observability, and AI threat protections. Owns roadmaps, security architecture, risk automation, compliance validation, and secure AI deployment across Azure, GCP, and AWS. Partners with AI, platform, and cybersecurity teams to deliver scalable security capabilities and develop a high-performing engineering organization.
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.
AI Security Platform Engineering
Qualifications- Design, build, and operate security capabilities for AI and generative AI platforms.
- Develop services, APIs, and workflows that automate AI risk detection, policy enforcement, and compliance validation.
- Integrate enterprise AI security technologies and frameworks into developer and platform engineering workflows.
- Support secure AI operations across model development, deployment, and monitoring environments.
- Design and develop secure web applications, dashboards, APIs, and backend services.
- Build security controls into frontend, backend, and API layers.
- Implement authentication, authorization, and access control solutions using modern identity standards (OAuth 2.0, OpenID Connect, JWT).
- Develop reusable services, SDKs, and APIs to enable secure-by-design adoption across engineering teams.
- Design and implement secure cloud-native architectures across Azure, Google Cloud Platform (GCP), and Amazon Web Services (AWS).
- Build and secure containerized and serverless applications using Kubernetes and cloud-native technologies.
- Implement Infrastructure as Code (IaC) using Terraform and embed security controls into cloud provisioning processes.
- Collaborate with platform teams to improve security posture across AI workloads and supporting infrastructure.
- Develop automation that continuously identifies, prioritizes, and remediates AI security risks.
- Integrate security controls into CI/CD pipelines, including code scanning, infrastructure scanning, secrets detection, and policy validation.
- Build observability solutions that provide logs, metrics, traces, monitoring, and alerting for AI platforms.
- Enable self-service security capabilities that reduce manual operational effort.
- Implement controls to address AI-specific risks such as prompt injection, sensitive data exposure, model misuse, and adversarial attacks.
- Conduct AI threat modeling and security architecture reviews.
- Partner with security operations teams to develop detection, monitoring, and response capabilities for AI systems.
- Evaluate emerging AI security technologies and drive adoption where appropriate.
- Lead, mentor, and develop a high-performing team of AI security engineers.
- Define technical roadmaps, architecture standards, and engineering best practices.
- Prioritize product capabilities based on business impact, risk reduction, and customer needs.
- Drive engineering excellence through design reviews, code reviews, and operational ownership.
- Secure, scalable AI security platform capabilities delivered on roadmap.
- Automated AI risk detection and policy enforcement integrated into engineering workflows.
- Enterprise observability, monitoring, and alerting for AI environments.
- CI/CD-integrated security controls adopted across AI engineering teams.
- Reusable APIs, SDKs, and automation services that enable secure-by-design development.
- A high-performing AI security engineering team delivering measurable business impact.
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.
- 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.
- 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.
- 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.
- 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.
- 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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