Requisition ID: Join a purpose driven winning team, committed to results, in an inclusive and high:performing culture. Overview: We are seeking a seasoned and strategic Senior AI/ML Engineer to join our Global Wealth Management Technology (GWMT) team. This role is ideal for a hands:on engineer with 5:7 years of experience in building scalable AI/ML systems, enabling production:grade workflows, and mentoring junior engineers. You will drive innovation at the intersection of financial data, cloud infrastructure, and advanced machine learning. Why Join Us: stylemargin:bottom:0.0cm;margin:top:0.0px: : Scotiabank's Global Wealth Management (GWM) division is dedicated to delivering personalized financial advice, investment management, and estate planning solutions to clients globally. : As part of this forward:thinking organization, youll help develop intelligent, secure, and scalable platforms that elevate both client and advisor experiences. : Your work will directly contribute to the next generation of wealth services through data:driven innovation and responsible AI practices. Key Responsibilities: stylemargin:bottom:0.0cm;margin:top:0.0px: : Lead the development, deployment, and monitoring of complex AI/ML models, including deep learning and natural language applications. : Build and manage scalable ML pipelines using tools like Airflow, dbt, or Google Dataflow for both batch and streaming data workloads. : Architect and optimize data workflows using modern data warehousing and lake technologies such as Databricks, BigQuery, Synapse, and Delta Lake. : Design and operate real:time pipelines using Kafka, Pub/Sub, or similar event:streaming platforms. : Implement and maintain robust CI/CD processes for ML using MLflow, Kubeflow, or equivalent orchestration tools. : Develop and deploy models on cloud:native infrastructure using Azure ML, Vertex AI, Docker, and Kubernetes. : Apply MLOps best practices for versioning, monitoring, retraining, and explainability. : Mentor L7 engineers, support code reviews, and contribute to a collaborative engineering culture. : Ensure secure model development, including encryption, RBAC, and compliance with GDPR, PIPEDA, and GLBA. : Collaborate with data engineers, architects, and product teams to translate financial business problems into machine learning solutions. Minimum Qualifications: stylemargin:bottom:0.0cm;margin:top:0.0px: : 5:7 years of professional experience delivering AI/ML solutions in production environments. : Proficiency in Python and ML libraries such as TensorFlow, PyTorch, Scikit:learn, and XGBoost. : Experience building or contributing to at least one production:grade RAG pipeline, using vector databases such as Azure Cognitive Search, GCP Vertex AI Matching Engine, FAISS, or Pinecone. : Hands:on experience with orchestration frameworks like LangChain, LlamaIndex, or Semantic Kernel for chaining context:aware agentic workflows. : Proven experience with data pipeline orchestration and ML lifecycle tools (e.g., Airflow, MLflow, Kubeflow). : Hands:on experience with cloud platforms (Azure, GCP) and container orchestration (Docker, Kubernetes). : Strong understanding of data architecture concepts, including ETL, streaming, and warehouse modeling. : Demonstrated experience mentoring junior engineers and leading technical initiatives. Preferred Skills: stylemargin:bottom:0.0cm;margin:top:0.0px: : Experience using managed vector search platforms like Azure AI Search and GCP Matching Engine in retrieval:augmented systems. : Proficiency in Terraform, Pulumi, or Azure Resource Manager (ARM) for infrastructure automation. : Familiarity with observability and monitoring stacks (Prometheus, Grafana, Azure Monitor, GCP Logging). : Knowledge of model explainability frameworks such as SHAP, LIME, or counterfactual analysis tools. : Prior exposure to