AI Security Architect

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