AI Security Architect - Lead Secure AI Lifecycle

Citizens Financial

United States

Hybrid

USD 150,000 - 210,000

Full time

4 days ago
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Job summary

Citizens Financial is seeking a Principal AI Security Engineer to lead security design for AI systems across the bank's technology stack. You will partner with engineering and architecture teams to deliver innovative, secure solutions that meet regulatory and risk requirements.

The role focuses on securing the full AI lifecycle—from data ingestion and model training to deployment and runtime—building safeguards against prompt injection, data leakage, and model misuse, while strengthening

Qualifications

  • 8+ years of data-intensive applications or data engineering experience.
  • Experience with large-scale tech enhancements and pivots.
  • Strong understanding of AI/ML patterns and DevOps practices.
  • Proficiency in Python and cloud development.
  • Bachelor's degree in Computer Science or related field.

Responsibilities

  • Collaborate with engineers and governance teams to align AI security with business goals.
  • Design, modify, develop and implement secure AI solutions and controls.
  • Infuse scalability, manageability and maintainability into distributed systems.
  • Evaluate, correct and prevent vulnerabilities during software development.
  • Lead incident response and monitor AI systems for anomalies and misuse.
  • Strengthen identity, access controls and secure integrations across cloud environments.

Skills

Data engineering
AI/ML patterns
DevOps
Python
Cloud development
APIs
Security
Architectural design

Education

Bachelor's degree in Computer Science

Tools

AWS
Azure

Job description

Citizens Financial is seeking a Principal AI Security Engineer to lead security design for AI systems across the bank's technology stack. You will partner with engineering and architecture teams to deliver innovative, secure solutions that meet regulatory and risk requirements.

The role focuses on securing the full AI lifecycle—from data ingestion and model training to deployment and runtime—building safeguards against prompt injection, data leakage, and model misuse, while strengthening

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