Director, GenAI & Machine learning, GFT

Job Description

What is the opportunity? As a Director, GenAI & Data Engineering, you will be responsible for leading a team of ML and Data engineers to build, enhance and maintain new and existing enterprise-wide production GenAI and Agentic AI Applications for the Global Functions Team in RBC
. The application domain ranges from Global Risk Management (GRM), Compliance, Global Reconciliation Services (GRS), HR, Finance etc.

This is a key role in our organization, and you will provide strategic leadership and direction to drive the effectiveness of the ML and Data Engineering team, enabling them to deliver high-value business outcomes through the application of AI.
What will you do?

Drive Innovation and New Application Development: Lead the ideation, technical validation, and end-to-end development and enhancements of new and existing high-impact AI-powered Agentic AI applications that solve critical business problems.

Define and Execute AI/ML Strategy: Develop and champion the strategic roadmap for integrating AI, Machine Learning, and Generative AI (GenAI) capabilities into core business applications, aligning technical initiatives with product roadmaps.

Lead by example as a hands-on technical mentor, guiding teams through expertise and collaboration.

Leadership and Talent Development: Hire, mentor, manage a multidisciplinary team of Data Scientists, Machine Learning Engineers, and Data Engineers, fostering a culture of technical excellence, experimentation, and accountability.

Enhance Existing Product Performance: Collaborate closely with Enterprise Gateway teams to identify opportunities to enhance current applications (e.g., personalization, forecasting, search relevance) using advanced ML techniques, driving measurable improvements in user experience or operational efficiency.

Champion Technical Standards and Tooling: Evaluate, select, and manage the technology stack, cloud resources, and tooling (e.g., specialized frameworks, compute infrastructure) necessary to support advanced AI/ML development and research at scale.

What do you need to succeed?
Must Have

Minimum 3-5+ years managing teams to deliver AI solutions

Master’s degree in computer science or a related technical field, or equivalent qualifications

Deep familiarity with the AI & ML lifecycle and the ability to confidently guide architects and engineers on the right technical path.

Proven experience across the entire research and development lifecycle, from concept prototyping to production-ready solutions, with a strong ability to engage with stakeholders to understand business needs and develop tailored solutions

Ability to translate complex technical possibilities into clear, quantifiable business outcomes.

An exceptional leader who can recruit, mentor, and retain top-tier Data Scientists and ML Engineers.

Ability to collaborate with Product, Front End, Architecture and Compliance teams.

Nice to Have

Multi-Cloud Platform experience

External technical thought leadership

Experience in a Finance organization.

What’s in it for you? We thrive on the challenge to be our best, progressive thinking to keep growing, and working together to deliver trusted advice to help our clients thrive and communities prosper. We care about each other, reaching our potential, making a difference to our communities, and achieving success that is mutual.

A comprehensive Total Rewards Program including bonuses and flexible benefits, competitive compensation, commissions, and stock where applicable

Leaders who support your development through coaching and managing opportunities

Ability to make a difference and lasting impact

Work in a dynamic, collaborative, progressive, and high-performing team

A world-class training program in financial services

Opportunities to do challenging work

Opportunities to take on progressively greater accountabilities

Opportunities to building close relationships with clients

Access to a variety of job opportunities across business and geographies.

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Job Skills
Big Data Management, Data Modeling, Data Science, Decision Making, Deep Learning, Machine Learning (ML), Predictive Analytics, Programming Languages, Relationship Building
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