Principal AI Engineer

Scotiabank; Canada; On-site; CAD 120,000 - 150,000; Full time

Job summary

A leading financial institution is seeking a Principal AI Engineer to design and integrate scalable AI/ML solutions. This role requires 7-9 years of experience in deploying AI systems and a deep understanding of cloud technologies. You will lead the development of complex ML Ops pipelines while ensuring compliance with industry regulations. Join a collaborative culture that values innovation and learning.

Benefits

Competitive rewards

Health benefits

Generous vacation

Employee share ownership

Qualifications

• 7–9 years of experience deploying AI/ML systems.
• Experience with financial regulations and compliance.
• Familiar with ML lifecycle tools.

Responsibilities

• Architect scalable ML solutions using cloud-native tech.
• Lead design and integration of AI systems.
• Develop advanced ML Ops pipelines.

Skills

AI/ML system architecture

Cloud-native technologies

CI/CD and ML Ops pipelines

Leadership in end-to-end architecture

Orchestration frameworks

Tools

Azure

GCP

Terraform

Docker

Kubernetes

Job description

Requisition ID: #

Join a purpose-driven, high-performing team committed to results in an inclusive culture.

Overview:

Global Wealth Management Technology (GWMT) is seeking an exceptional and forward-thinking Principal AI Engineer to 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:

• 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’ll be helping define the future of AI in wealth management, working with industry veterans and passionate technologists.
• Our engineering culture values autonomy, intellectual rigor, and business alignment, building intelligent platforms integrated across the advisory experience.
• You’ll have opportunities to contribute to impactful, scalable work that solves hard problems.

Key Responsibilities:

• 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.
• Translate evolving business objectives and regulatory requirements into AI frameworks and platform capabilities.
• Own technical vision around explainability, privacy, compliance (e.g., PIPEDA, GLBA, GDPR), and governance.
• Collaborate with engineering leads, enterprise architects, and data teams to establish resilient, performant, and measurable ML infrastructure.
• Mentor engineers and guide architectural reviews, solution scoping, and delivery excellence.

Minimum Must-Have Qualifications:

• 7–9 years of experience building and deploying AI/ML systems in production, with leadership in end-to-end architecture.
• Experience with financial services data, models, and regulatory compliance (e.g., explainability, auditability).
• Strong experience with retrieval-augmented generation (RAG) systems and vector databases (Azure Cognitive Search, GCP Matching Engine, FAISS, or Weaviate).
• Proficiency with orchestration frameworks such as LangChain, LlamaIndex, or Semantic Kernel.
• Hands-on experience with infrastructure-as-code tools like Terraform, ARM, or Pulumi.
• Deep understanding of ML lifecycle tools (Airflow, MLflow, Kubeflow) and model registry/versioning.
• Experience with observability stacks (Prometheus, Grafana, Azure Monitor, GCP Cloud Logging).
• Cloud-native deployment skills (Azure ML, Vertex AI) and containerization (Docker, Kubernetes).

Preferred Skills:

• Experience designing federated learning or privacy-preserving ML architectures.
• Familiarity with AI risk and controls frameworks (model validation, fairness auditing, robustness).
• Ability to present and defend architectural decisions to technical and executive audiences.
• Contributions to open-source ML/AI tools or research publications.
• Experience shaping platform strategies in enterprise environments.

What’s In It for You:

• Work with industry leaders from top tech companies.
• Foster an environment of innovation, continuous learning, and collaboration.
• Enjoy a safe, inclusive environment with fair treatment and anti-racism initiatives.
• Competitive rewards, bonus, pension, employee share ownership, generous vacation, health benefits, and banking privileges.
• State-of-the-art collaboration environments when in person.

Locations:

Canada: Ontario - Toronto; Quebec - Montreal

About Scotiabank:

Guided by our purpose: "for every future", we help our customers and communities succeed through a broad range of financial services.

We value diverse skills and experiences and are committed to an inclusive, accessible environment. If you require accommodations during recruitment, please inform our team. Only candidates considered for an interview will be contacted.