You demonstrate technical fluency in AI/ML. You understand predictive modeling, NLP, computer vision, generative AI, and Agentic AI, and you’ve worked across the production lifecycle assessing controls and evidence in regulated environments. You securely navigate AI risk frameworks and regulation. You’re fluent with NIST AI RMF, ISO/IEC AI risk standards, such as 23894 and 42001, and model risk expectations in financial services, and you translate them into pragmatic, auditable controls. You apply an AI risk engineering mindset. You understand how AI risks can be mitigated through technical and process controls, including guardrails, access controls, monitoring, evaluation, testing, traceability, human-in-the-loop controls, and lifecycle governance. You can work with technology teams to assess whether controls are appropriately designed, implemented, evidenced, and monitored. You help embed governance expectations early in the AI lifecycle by supporting reusable control patterns, pre-approved guardrails, technical standards, and practical governance checkpoints that enable responsible innovation without creating unnecessary friction. You understand emerging Agentic AI governance considerations. You can assess risks associated with AI agents, autonomous workflows, tool use, memory, external connectivity, prompt injection, overreliance, human oversight, and escalation paths, and you can help define appropriate controls for safe and accountable deployment. You uphold Trustworthy AI as a core principle, consistently applying data protection, fairness and bias mitigation, explainability, security-by-design, and third-party risk management to safeguard clients and the bank, while supporting positive outcomes for society.
You’re a certified professional and have a degree/diploma in . a relevant field, such as Data Science, Computer Science, Engineering, Statistics, or Risk Management. Certifications such as IAPP AIGP, ISO/IEC 42001, ISO/IEC 23894, CertNexus CEET, IEEE CertifAIEd, or IAPP CIPT are assets.
