Lead AI Security Architect
Rockwell Automation
full-time
Posted on:
Location Type: Hybrid
Location: Milwaukee • Ohio, Texas, Wisconsin • 🇺🇸 United States
Visit company websiteJob Level
Senior
Tech Stack
AWSAzureCloudCyber SecurityGoogle Cloud PlatformSDLCTensorflow
About the role
- Develop the enterprise AI security architecture
- Align it with our goals, AI governance frameworks (e.g., NIST AI RMF, ISO/IEC 42001), and cybersecurity standards (e.g., NIST CSF, ISO 27001, IEC 62443)
- Define secure architectures for AI/ML model development, deployment, and integration with enterprise data and cloud platforms
- Establish security reference architectures for GenAI, LLMOps, MLOps, and AI-driven automation
- Conduct AI threat modeling, risk assessments, and red teaming for AI/ML systems
- Find and address AI-specific risks such as model inversion, prompt injection, data poisoning, and adversarial attacks
- Support compliance with the latest AI security and ethics regulations (e.g., EU AI Act, U.S. Executive Orders on AI, sector-specific standards)
- Guide data scientists and developers on implementing secure model training, validation, and inference pipelines
- Partner with enterprise architects to integrate AI trust controls (authenticity, traceability, explainability, and accountability) into platforms and services
- Evaluate and deploy AI security tools for model protection, data governance, and AI behavior monitoring
- Collaborate with product security, DevSecOps, and data engineering teams to embed AI security into the SDLC and CI/CD pipelines
- Work with legal, risk, and compliance teams to establish AI acceptable use, data residency, and model governance policies
- Lead security reviews and architecture boards for AI-enabled projects
- Stay current on AI cybersecurity research, frameworks, and the latest AI threats
- Develop best practices and strategies for responsible AI security and assurance
- Mentor junior architects and engineers in AI and cybersecurity principles.
Requirements
- Bachelor's Degree or equivalent years of relevant work experience
- Legal authorization to work in the U.S.
- Ability to travel up to 10%
- Typically requires 12+ years of relevant experience in cybersecurity architecture
- 3+ years focused on AI/ML or data science security
- Advanced degree in Computer Science, Engineering, Cybersecurity, or related field
- Experience with AI/ML pipelines, MLOps, Model Context Protocol (MPC), Agentic Identity, and cloud-native architectures (AWS SageMaker, Azure ML, GCP Vertex AI)
- Expertise in data protection, identity and access management, encryption, and secure software development
- Knowledge of AI threat landscapes, adversarial machine learning, and model integrity protection
- Experience with compliance frameworks such as NIST AI RMF, ISO/IEC 42001, and data privacy regulations (GDPR, CCPA)
- Professional certifications such as CISSP, CISM, CCSP and enterprise architecture certifications
- AI/ML certifications (e.g., TensorFlow, AWS ML Specialty, Microsoft Azure AI Engineer)
- Hands-on experience with secure LLM deployments and GenAI security testing
- Experience in OT or industrial AI environments (IEC 62443 knowledge).
Benefits
- Health Insurance including Medical, Dental and Vision
- 401k
- Paid Time off
- Parental and Caregiver Leave
- Flexible Work Schedule where you will work with your manager to enjoy a work schedule that can be flexible with your personal life.
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
AI security architectureAI/ML model developmentAI threat modelingrisk assessmentsdata protectionidentity and access managementencryptionsecure software developmentMLOpscloud-native architectures
Soft skills
mentoringcollaborationleadershipcommunicationguidance
Certifications
CISSPCISMCCSPAI/ML certificationsTensorFlowAWS ML SpecialtyMicrosoft Azure AI Engineerenterprise architecture certifications