AI Security Architect

Cadence Design Systems•San Jose, CA

3d•Onsite

About The Position

At Cadence, we hire and develop leaders and innovators who want to make an impact on the world of technology. Cadence InfoSec is seeking a Security Architect with deep expertise in Artificial Intelligence (AI) and Machine Learning (ML) security to design, implement, and govern secure AI systems across the enterprise. This role will focus on protecting AI/ML models, data pipelines, and GenAI applications from emerging threats while enabling safe innovation.

Requirements

Bachelor’s or Master’s degree in Computer Science, Cybersecurity, or related field
8+ years in cybersecurity architecture or engineering
Experience securing AI/ML systems or data platforms
Strong understanding of: Cloud security (IAM, network, containers, serverless)
API security and microservices
Encryption, key management, and identity systems
Development of Agent and Agentic AI for security use cases
Experience with MCP

Nice To Haves

Experience with LLMs (e.g., prompt engineering, RAG architectures)
Familiarity with adversarial ML techniques
Knowledge of tools like: MLflow, Kubeflow, SageMaker
SIEM/XDR platforms
Certifications: CISSP, CCSP, or cloud security certifications
Experience in semiconductor industry is a plus

Responsibilities

AI/ML Security Architecture: Design secure architectures for AI/ML systems, including model training, inference, and deployment pipelines. Define security controls for LLMs (Large Language Models), GenAI platforms, and AI APIs. Embed security into MLOps pipelines (DevSecOps for AI).
Threat Modeling & Risk Management: Conduct threat modeling for AI systems (e.g., prompt injection, model poisoning, data leakage). Develop risk frameworks aligned with NIST AI Risk Management Framework. Identify and mitigate adversarial AI threats and abuse cases.
Data Security & Privacy: Ensure protection of training and inference data (PII, PHI, proprietary data). Implement data governance, anonymization, and encryption strategies. Ensure compliance with regulations (GDPR, HIPAA, etc.).
Cloud & Platform Security: Secure AI workloads across cloud platforms such as Amazon Web Service, Microsoft Azure, Google Cloud, IBM Cloud. Architect secure integrations with AI services and APIs.
Model Security & Integrity: Protect against model theft, inversion, and extraction attacks. Implement model monitoring for drift, anomalies, and abuse. Ensure secure model storage, versioning, and access control.
Governance & Compliance: Establish AI security policies, standards, and guardrails. Align with industry AI frameworks such as ISO AI standards (e.g., ISO/IEC 42001). Support audit, regulatory, and CIO and CISO reporting.
Collaboration & Leadership: Partner with data scientists, ML engineers, and product teams. Provide security guidance for AI product development. Lead security reviews and architecture boards. Mentor security engineers on AI-specific threats.