Requisition ID: Join a purpose driven winning team, committed to results, in an inclusive and high:performing culture. Overview: Global Wealth Management Technology (GWMT) is seeking an exceptional and forward:thinking Principal AI/ML Engineer to help shape and execute the next generation of intelligent solutions within GWMT. This role is ideal for a highly competent engineer with 7:9 years of hands:on experience who can architect robust AI/ML systems, align with enterprise strategy, and elevate technical excellence across domains. You will play a pivotal role in translating business vision into AI:driven outcomes, championing scalable design, and fostering a collaborative engineering culture. Why Join Us: stylemargin:bottom:0.0cm;margin:top:0.0px: : Scotiabank's Global Wealth Management (GWM) division empowers millions of clients worldwide with personalized financial planning, investment strategies, and advisory services. : As part of GWMT, you'll work on high:impact problems that blend financial insight with advanced machine learning. You will shape cloud:native, secure, and explainable AI/ML systems that enhance advisor intelligence and client experience-while aligning to regulatory, operational, and strategic priorities. : You won't just be joining a team-you'll be helping define the future of AI in wealth management. : Our engineering culture values autonomy, intellectual rigor, and business alignment. We're building intelligent platforms that integrate with every layer of our advisory experience. : You'll collaborate with industry veterans and technologists who are passionate about impact, precision, and doing meaningful work at scale. : If you're looking to shape architectures that endure and solve hard problems that matter-this is your runway. Key Responsibilities: stylemargin:bottom:0.0cm;margin:top:0.0px: : Architect scalable and secure ML solutions using cloud:native technologies (Azure, GCP) and distributed processing frameworks. : Lead the design, integration, and optimization of complex AI/ML systems-including RAG, agentic workflows, and multi:model orchestration. : Develop and maintain advanced ML Ops pipelines with robust CI/CD, testing, retraining, and observability baked in. : Translate evolving business objectives and regulatory requirements into practical AI frameworks and platform capabilities. : Own technical vision and best practices around explainability, privacy, compliance (e.g., PIPEDA, GLBA, GDPR), and governance. : Collaborate closely with engineering leads, enterprise architects, and data teams to establish end:to:end ML infrastructure that is resilient, performant, and measurable. : Mentor senior and junior engineers alike, guiding architectural reviews, solution scoping, and delivery excellence. Minimum Must have Qualifications: stylemargin:bottom:0.0cm;margin:top:0.0px: : 7:9 years of experience building and deploying AI/ML systems in production, with a track record of leading end:to:end architecture. : Demonstrated experience with financial services data, models, and regulatory environments (e.g., compliance logging, auditability, explainability). : Strong applied experience with retrieval:augmented generation (RAG) systems and vector databases, including Azure Cognitive Search, GCP Matching Engine, FAISS, or Weaviate. : Proficiency in orchestration frameworks such as LangChain, LlamaIndex, or Semantic Kernel. : Hands:on expertise in infrastructure:as:code tools such as Terraform, ARM, or Pulumi. : Deep understanding of ML lifecycle tools, including Airflow, MLflow, Kubeflow, and model registry/versioning. : Expertise in observability stacks such as Prometheus, Grafana, Azure Monitor, or GCP Cloud Logging. : Cloud:native deployment skills (Azure ML, Vertex AI) and containerization (Docker, Kubernetes). Preferred Skills: stylemargin:bottom:0.0cm;margin:top:0.0px