Senior Director AI and Operations Engineering

A career as Senior Director, AI and Operations Engineering in the Wealth Management Operations team at National Bank means acting as a leader in accelerating the transformation of wealth management operations by designing and deploying hyper-automation solutions that combine AI, automation, software development and agent technologies to simplify processes, increase operational capacity, improve the client experience and employé.es and sustainably reduce the cost of service.

You act as a field technology leader, able to identify high-value opportunities, select the right technology, rapidly prototype and industrialize secure, resilient and economically viable solutions. It is by relying on your experience in transformation, technology and with your knowledge of the investment sector and/or capital markets that you have a positive impact on the experience of our users, our employees, as well as on our ability to be a growth partner of choice for our organization.

Your job

• Identify processes or automation, AI where agentic capabilities can drive the most value
• Choose the simplest technology that effectively solves the problem rather than always applying the same technology
• Remain concretely involved on the technical level and be able to quickly transform an operational issue into a functional solution
• Accelerate process transformation, drive adoption of new solutions, and drive initiatives from idea to prototype, ensuring delivery of expected benefits
• Ensure the economic viability of solutions: infrastructure, licenses, consumption, model costs and value generated
• Building solutions where security, privacy, traceability and operational resilience are "Build by design"
• Develop strong engineering expertise within operations to accelerate the adoption of automation and artificial intelligence in their ways of doing things
• Generate business value through automation and optimization of operational capacity by ensuring the quality of solutions and controlling the risks associated with automation
• Monitor the performance of solutions and measure the gains made

Your team

Within the Wealth Management Operations sector, you are part of a team of 650 colleagues and report to the Vice-President, Wealth Management Operations.

As a leader, you lead an engineering team of about 12 people, composed of two directors, developers and citizen developers, directly integrated into operations.

Our team stands out for our expertise, our understanding of our processes, our proximity to our business lines and our technology teams, our rigor, our ambition to be the best operational center and our "One team" culture. Being part of our team means being part of a winning team that succeeds in combining pleasure, ambition and results.

You benefit from a hybrid work model with a required presence in the office three days a week (minimum) and the option to work remotely the other two days, promoting both in-person collaboration and flexibility.

You will be required to travel occasionally between Montreal and Toronto in order to promote close collaboration with teams and partners.

We favor a variety of forms of continuous learning to enrich your development, including learning in action, training content made available to you and working in collaboration with colleagues with diverse expertise and profiles.

Prerequisites

• Bachelor's degree in computer science, software engineering, artificial intelligence, data science or related technology field or any other combination of education and experience deemed equivalent
• 7 to 10 years of relevant experience in managing operational and technological teams, including management of managers.
• Experience in the wealth management and/or capital markets industry.
• Recognized experience in organizational transformation, process optimization and continuous improvement, particularly in the management of major changes involving operational, technological, human and risk management issues.
• Strong knowledge of software development, Python, APIs, integration, and cloud architecture, as well as modern software engineering practices such as Git, CI/CD, testing, and monitoring.
• Expertise in automation and artificial intelligence, including workflows, RPA, low-code/no-code solutions, generative AI, large language models (LLMs) and agentic technologies.
• Good understanding of the economics of AI, including model consumption, inference costs, and trade-offs between performance and cost.
• Good knowledge of the principles of cybersecurity, data protection and responsible AI, including access management, auditability and resilience.
• Builder mentality, strong operational acumen and mobilizing leadership, with an ability to experiment quickly, simplify processes, collaborate with subject matter experts and evolve prototypes into secure, resilient and deployable solutions at scale.