Principal AI Security Engineer

Citizens•Johnston, RI

10d•$150,000 - $190,000•Hybrid

About The Position

The Principal AI Security Engineer works with engineering teams and architecture to produce high-quality technology solutions, with autonomy to lead, design, and develop solutions to significant technical challenges in banking. This peer-leader pursues innovative initiatives and helps advance customer experience and technology across Citizens’ operations. The role builds and operationalizes security controls for AI-powered applications, protecting models, prompts, outputs, data, and integrations throughout the AI lifecycle—from ingestion and training through deployment and runtime. It addresses emerging AI threats, strengthens identity and access controls, protects sensitive data, hardens cloud infrastructure, monitors AI systems, and integrates automated security testing. The position also evaluates third-party AI services, leads AI-related incident response, and collaborates with architects, developers, and governance teams to align security practices with business objectives and regulatory requirements. This is a hybrid role requiring four days onsite and one day remote per week at one of the listed hubs. Remote-only work is not available. The stated salary range is $150,000 to $190,000 per year, with an opportunity for additional incentive earnings. Actual pay depends on factors including budget, work location, relevant skills, and experience.

Requirements

At least 8 years of working experience in data-intensive applications or data engineering.
Experience implementing large-scale technological enhancements and/or pivots, including pilot implementation and analysis.
Excellent understanding of AI/ML patterns and techniques.
Excellent understanding of DevOps.
Understanding of Python and at least one other programming language.
Experience working with IT infrastructure and cloud development.
Experience with APIs.
Experience designing highly available and resilient solutions, automating work, and identifying performance-improvement opportunities.
Bachelor’s degree or foreign equivalent in computer science or a related field.
Experience in two or three of these technology areas: infrastructure, security, DevOps, application development, database technologies, or cloud computing.

Nice To Haves

Working experience in the financial services industry.
Bachelor’s degree in computer science, computer engineering, or a related discipline.
Executive speaking and presentation skills, including formal presentations, whiteboarding, and presentations to large and small groups.
Preferred degree in software engineering, computer science, engineering, mathematics, or a related discipline.

Responsibilities

Collaborate with engineering teams and architecture on solution design, contributing insight while ensuring product success and adherence to enterprise architecture principles.
Design, modify, develop, and implement software solutions, including modern components, tools, and applications that support strategic business goals.
Incorporate scalability, manageability, and maintainability into distributed service-based system designs.
Apply industry best practices to evaluate, correct, and prevent software vulnerabilities.
Serve as a peer leader who encourages innovation and accountability.
Build and operationalize security controls for AI-powered applications, making models, prompts, and outputs resilient against misuse and abuse.
Secure the full AI lifecycle, from data ingestion and model training through deployment and runtime, across internal applications and external platforms.
Engineer safeguards against AI threats such as prompt injection, data poisoning, model inversion, and unintended data leakage.
Strengthen identity and access controls for AI systems, including service identities, API protection, and least-privilege access across SaaS and cloud environments.
Secure integrations between AI components and enterprise systems, including databases, APIs, and SaaS tools, to prevent unauthorized access and data exfiltration.
Protect sensitive data used by AI systems through encryption, tokenization, and privacy-preserving techniques across hybrid and cloud-native architectures.
Monitor AI systems and agentic behaviors for anomalies, policy violations, and malicious activity using logging and detection engineering practices.
Harden cloud infrastructure supporting AI workloads, including containers, orchestration platforms, and serverless services, through secure configurations.
Evaluate and manage third-party AI SaaS risks, including data handling, access control, and vendor security posture.
Integrate automated security testing into AI development pipelines, including adversarial testing and model-robustness validation.
Lead incident response for AI systems, investigating misuse, security-related model drift, and potential data exposure events.
Collaborate with architects, developers, and governance teams to align AI security practices with business objectives and regulatory requirements.