The Artificial Intelligence (AI) team is responsible for driving automation, data-driven decision making and software modernization using AI centric tools. Currently, we leverage machine learning, natural language processing, predictive analytics, and generative AI to enhance customer experience, and improve operational efficiency. This includes utilizing fraud protection software to ensure regulatory and system availability.
As an AI Governance & Security Architect, you will be responsible for developing and leading enterprise frameworks that ensure the ethical, secure, and compliant adoption of AI within the financial organization. You will bridge cybersecurity, risk management, and AI lifecycle governance to safeguard sensitive data and models against threats, while meeting regulatory obligations. You will also provide strategic leadership across IT, risk, compliance, and business functions to embed responsible AI practices, strengthen resilience against AI-specific risks, and enable innovation in a secure and controlled manner. This is a subset of the overall responsibilities which will include multiple initiatives as assigned by IT leadership.
This role is hybrid (Mon through Thu on-site / Fri remote)
How you will spend your time:
- Define and implement enterprise-wide AI governance frameworks to ensure responsible, ethical, and compliant use of AI in financial operations.
- Develop and enforce AI security standards aligned with regulatory requirements (e.g., FFIEC, OCC, GLBA, GDPR, ISO 42001, NIST AI RMF).
- Partner with risk, compliance, and legal teams to create policies covering AI model lifecycle management, bias detection, explainability, and auditability.
- Oversee secure AI/ML solution deployment in both on-premises and cloud environments (AWS, Azure, GCP), ensuring robust data protection and encryption practices.
- Conduct AI security risk assessments, threat modeling, and red team testing for generative AI and predictive models.
- Establish monitoring frameworks for AI systems to track drift, anomalies, and adversarial threats.
- Provide architectural guidance on integrating AI platforms with existing banking systems (ACH, RTP, Wires, Core Banking, Payment Hubs).
- Lead AI security incident response and ensure remediation processes meet financial regulatory standards.
- Serve as a subject matter expert for internal stakeholders on AI governance, regulatory compliance, and ethical AI adoption.
- Mentor junior architects and engineers in AI governance and cybersecurity best practices.
We are excited to talk if you have:
- Bachelor’s degree in Computer Science, Cybersecurity, Data Science, or related field and at least 7 years of professional experience in enterprise architecture, information security, or technology governance in financial services, OR equivalent combination of education and work experience.
- Proven track record of implementing governance frameworks, risk management strategies, and compliance programs for emerging technologies.
- Familiarity with financial services regulations and security requirements.
- Experience collaborating with auditors, regulators, and compliance teams.
- Strong understanding of AI/ML lifecycle management, including model development, validation, deployment, and monitoring.
- Knowledge of data governance principles, MDM (Golden Record, Canonical Models), and regulatory compliance in financial services.
- Proficiency in cloud-native AI/ML platforms (AWS SageMaker, Bedrock, Azure AI, GCP Vertex).
- Familiarity with security frameworks (NIST, ISO 27001/42001, CIS Controls).
- Ability to translate complex AI and security concepts into business-friendly language.
Bonus Points if you have:
- Master’s degree in Information Security, Artificial Intelligence, or related discipline.
- At least 10 years of experience in financial services or regulated industries.
- Demonstrated leadership in AI governance, cybersecurity, or enterprise architecture initiatives.
- Certifications such as CISSP, CISM, ISO 42001 Lead Auditor, or Certified AI Governance Professional.
- Experience presenting to executive leadership and regulators.
- Hands-on experience with AI observability tools (e.g., Fiddler, Arize, MLflow).
- Familiarity with secure federated learning, differential privacy, and adversarial ML defenses.
- Experience leading cross-functional AI security initiatives in regulated industries.
- Advanced knowledge of cryptography, data anonymization, and AI-driven fraud detection techniques.
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