Director, AI Engineering

Royal Bank of Canada•Toronto, ON

2d•Onsite

About The Position

We are seeking a visionary and execution-focused Director of AI Engineering to lead the development and deployment of scalable AI and data-driven solutions. In this role, you will define the AI engineering strategy, build a high-performing team, and establish modern platforms and practices that accelerate innovation and business value. You will collaborate closely with business leaders, product teams, data organizations, and technology partners to operationalize AI capabilities that are secure, reliable, and measurable. This is an opportunity to shape the future of enterprise AI adoption while driving tangible outcomes through advanced analytics, machine learning, generative AI, and intelligent automation.

Requirements

Bachelor’s degree in Computer Science, Software Engineering, or a related field
10+ years of experience in software engineering, data engineering, machine learning, or AI solution delivery, including leadership experience managing technical teams.
Proven experience designing and deploying enterprise-scale AI/ML platforms and production AI applications in cloud environments.
Strong understanding of AI engineering practices including MLOps, LLMOps, model deployment, observability, governance, and automation.
Experience leading cross-functional initiatives that integrate AI capabilities into business processes, products, or customer experiences.
Deep technical knowledge of modern AI ecosystems, including machine learning frameworks, generative AI technologies, APIs, vector databases, and cloud-native architectures.
Exceptional communication and stakeholder management skills with the ability to influence executive leaders and translate complex technical concepts into business value.
A passion for innovation, continuous learning, and building inclusive, collaborative, and high-performing engineering cultures.

Responsibilities

Define and lead the enterprise AI engineering strategy, roadmap, architecture standards, and delivery model aligned to business priorities.
Build, mentor, and scale high-performing AI engineering and MLOps teams focused on delivering production-grade AI solutions.
Establish scalable AI platforms, frameworks, and governance practices to support machine learning, generative AI, and data science workloads.
Partner with cross-functional stakeholders to identify high-value AI use cases and drive end-to-end solution delivery from experimentation to production.
Ensure operational excellence through robust AI lifecycle management, model monitoring, security, compliance, and responsible AI practices.