Director, AI Platform

About the role

The Director, AI Platform role is within Global Banking and Markets (GBM), a leading Canadian Capital Markets and Investment Banking business with a growing platform in the US and Latin America. GBM operates globally for over 100 years and provides a full range of investment banking, credit, and risk management products and services relevant to the financing and strategic development needs of clients.

Responsibilities

• Define and deliver the enterprise AI platform strategy, roadmap, and engineering execution model aligned to business priorities, technology standards, and responsible AI requirements.
• Build reusable platform capabilities that enable teams to develop, test, deploy, and operate AI solutions consistently and securely across the enterprise.
• Establish scalable frameworks for: Model, foundation model, and large language model enablement; Agentic AI orchestration, workflow automation, and tool integration; Prompt, context, retrieval, and knowledge grounding services; Reusable APIs, SDKs, templates, and reference patterns for AI engineering teams.
• Implement enterprise-grade AI platform controls including: Secure access, identity, entitlement, and policy enforcement for AI services; Responsible AI guardrails, safety patterns, evaluation gates, and human-in-the-loop controls; Auditability, traceability, model usage tracking, and evidence generation.
• Lead high-performing platform engineering teams, setting clear technical direction, delivery standards, and operating rhythms.
• Partner with application, data, cloud, cyber, risk, and architecture teams to embed AI platform capabilities into enterprise delivery workflows.
• Ensure the AI platform supports regulated use cases by design, with controls integrated into engineering pipelines rather than applied as after-the-fact reviews.
• Establish and deliver an AI operations framework that enables reliable, measurable, and governed AI services in production.
• Deliver platform capabilities for: Model and agent monitoring, performance tracking, and drift detection; Evaluation, red-teaming support, quality scoring, and regression testing; Cost, token, capacity, and usage observability across AI workloads; Incident management, rollback patterns, and continuous improvement of AI services.
• Embed testing, monitoring, and governance checks into AI delivery pipelines to ensure trust, resiliency, and operational readiness by design.
• Create a platform experience that makes AI capabilities easy to discover, consume, and reuse across engineering and business teams.
• Enable governed reuse through: AI service catalogs, reusable components, and approved reference architectures; Standard onboarding patterns, developer documentation, and self-service capabilities; Reusable evaluation datasets, prompt libraries, and implementation blueprints.
• Drive adoption of AI platform capabilities by partnering with product, engineering, architecture, and business stakeholders to convert high-value AI use cases into scalable enterprise patterns.

Requirements

Bachelor’s degree in computer science, engineering, information technology, data science, or a related technical discipline. Experience in financial services or other highly regulated industries, with a strong understanding of security, risk, compliance, and operational control expectations. 10+ years of technology and engineering experience, including 5+ years leading platform, AI, data, cloud, or enterprise engineering teams. Hands-on leadership experience with: AI, machine learning, generative AI, or agentic AI platforms; Cloud-native platform engineering, APIs, microservices, CI/CD, and infrastructure automation; Model deployment, orchestration, monitoring, evaluation, and operational support patterns; Strong understanding of responsible AI, AI governance, model risk, security, privacy, and regulatory expectations for production AI systems. Deep expertise in platform engineering practices, AI delivery lifecycle, software engineering excellence, and operating production-grade services. Strong understanding of: AI security, privacy, responsible AI, model lifecycle management, and regulatory compliance in a financial services environment. Proven ability to work directly with engineers, architects, product leaders, data scientists, risk partners, and senior stakeholders. Exceptional communication skills with the ability to translate AI platform strategy into clear engineering priorities, executive narratives, and measurable business outcomes.