Vice President,AI Platform Engineer Job Toronto area,Ontario Canada,IT/Tech

Requisition

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

Position:
Vice President, AI Platform Engineering

The VP, AI Platform Engineering is accountable or building, owning, and operating Scotiabank’s enterprise AI platform—a secure, scalable, governed, and reusable foundation enabling GenAI, predictive AI, agentic AI, and automation use cases across all business lines.

This role leads the end to end engineering, operationalization, and lifecycle management of the Bank’s AI platform, ensuring consistent delivery patterns, modern MLOps/LLMOps capabilities, model hosting, data/feature access, observability, and integration with enterprise controls. This role will accelerate the Bank’s shift from experimental AI to enterprise wide, production ready, risk aligned AI.

Key Accountabilities:

Enterprise AI Platform Strategy & Architecture

Define and own the AI Platform Strategy, ensuring alignment with the enterprise AI vision, hub and spoke operating model, and modernization roadmap. Translate enterprise AI principles into platform level capabilities spanning model development, training, serving, observability, metadata, pipelines, vector databases, and GPU/compute orchestration.

Develop and implement the reference architectures and “golden paths” for AI development and deployment, consistent with internal AI engineering patterns and emerging best practices

Partner with Enterprise Architecture to ensure platform designs integrate with crossbank standards, security controls, and cloud architecture

AI Platform Build, Run & Modernization

Lead the engineering, delivery, and operations of all AI platform components, including: ML/LLM training environments, managed feature stores, Model repositories and registries, agentic AI frameworks etc.

Own end to end platform reliability, scalability, performance, and availability (SLAs/SLOs), consistent with expectations for enterprise platforms.

Drive modernization—reducing fragmentation, integrating isolated AI tooling, removing duplicative infrastructures, and future proofing architecture.

MLOps / LLMOps Excellence & Automation

Establish enterprisegrade MLOps and LLMOps foundations, ensuring standardized, automated:

Training pipelines

Feature engineering workflows

Evaluation, drift detection, and monitoring

Deployment and rollback patterns

Continuous integration and delivery (CI/CD) for models

Build golden paths that significantly reduce time to production and increase reusability across use cases.

AI Governance, Security, & Risk Alignment

Ensure platform compliance with AI governance requirements including:

Security controls

Data classification

Model risk management

Privacy and consent o Responsible AI frameworks

Audit and regulatory expectations

Partner with AI Risk, Compliance, CDO, and Legal to ensure AI capabilities adhere to enterprise controls

Build and lead a high-performing data leadership team, develop talent across data engineering, data architecture, and data governance, and foster a culture of accountability, quality, and continuous improvement.

Directs day-to-day activities in a manner consistent with the Bank’s risk culture and the relevant risk appetite statement and limits.

Communicates the Bank’s risk culture and risk appetite statement throughout their teams.

Creates an environment in which their team pursues effective and efficient operations of their respective areas in accordance with Scotiabank’s Values, its Code of Conduct and the Global Sales Principles, while ensuring the adequacy, adherence to and effectiveness of day-to-day business controls to meet obligations with respect to operational, compliance, AML/ATF/sanctions and conduct risk.

Builds a high performance environment and implements a people strategy that attracts, retains, develops and motivates their team by fostering an inclusive work environment and using a coaching mindset and behaviours; communicating vison/values/business strategy; and, managing succession and development planning for the team

Education & Experience

15+ years in engineering leadership with deep specialization in AI/ML platform engineering, distributed systems, cloud infrastructure, and automation.

Demonstrated experience operating enterprise AI or ML platforms at scale (multi cloud preferred).

Expert knowledge of: MLOps, LLMOps, model observability GPU orchestration, vector databases, embeddings, agentic AI frameworks Cloud native architecture (Azure, GCP preferred) Data and model governance frameworks

Track record of leading multi disciplinary engineering teams (100+), preferably in regulated industries.

Strong executive communication, organizational design, and stakeholder alignment skills.

Work in a standard office-based environment; non-standard hours are a common occurrence.

Limited travel domestically and globally


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