Job description

At Cadence, we hire and develop leaders and innovators who want to make an impact on the world of technology. Cadence InfoSec 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. Key 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 Required Qualifications 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 Preferred Qualifications 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 Key Skills AI Threat Modeling (Prompt Injection, Data Poisoning, Model Evasion) Secure MLOps / DevSecOps Zero Trust Architecture Data Privacy & Governance Cloud-Native Security Risk & Compliance Management The annual salary range for California is $164,500 to $305,500. You may also be eligible to receive incentive compensation: bonus, equity, and benefits. Sales positions generally offer a competitive On Target Earnings (OTE) incentive compensation structure. Please note that the salary range is a guideline and compensation may vary based on factors such as qualifications, skill level, competencies and work location. Our benefits programs include: paid vacation and paid holidays, 401(k) plan with employer match, employee stock purchase plan, a variety of medical, dental and vision plan options, and more. We’re doing work that matters. Help us solve what others can’t.

Requirements

Must-haves

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
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

Nice-to-haves

Experience with LLMs (e.g., prompt engineering, RAG architectures)
Familiarity with adversarial ML techniques
Experience in semiconductor industry is a plus

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningmlazureapi

Platform: workday
Effort: long

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)