AI Security Architect
Role details
Job location
Tech stack
Job description
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
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
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, * AI Threat Modeling (Prompt Injection, Data Poisoning, Model Evasion)
- Secure MLOps / DevSecOps
- Zero Trust Architecture
- Data Privacy & Governance
- Cloud-Native Security
- Risk & Compliance Management
Benefits & conditions
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.