Director of AI/ML Engineering: LLMs, RAG & Knowledge Graphs

RBC; Toronto; On-site; CAD 140,000 - 190,000; Full time

Benefits offered by this job

Bonus and flexible benefits

Leaders supporting your development

Flexible work/life balance

World-class training

Job summary

RBC in Toronto is seeking a Director of Engineering to shape the future of our digital platform by leading embedding models to transform data into vectorized representations, enabling semantic understanding and contextual relevance in AI-driven applications.

You will oversee knowledge graphs integration, drive RAG techniques for real-time data retrieval, and govern LLM deployment while maintaining enterprise compliance.

Qualifications

• 5+ years of leadership experience managing engineers, data scientists, or similar roles.
• 8+ years with RDBMS and data technologies (e.g., Snowflake).
• 5+ years hands-on with Python, React, and REST APIs.
• 2+ years hands-on with AI technologies (LLMs, ML).
• Deep understanding of security protocols and interface specs.
• Proficiency in DevOps, CI/CD pipelines and automation tools.

Responsibilities

• Lead by example by writing clean, scalable front-end and back-end code for critical systems.
• Drive adoption of next-gen AI-driven solutions and embedding models.
• Mentor and develop engineers, fostering curiosity and excellence.
• Design high-performance, secure, scalable applications with reliability.
• Champion CI/CD, cloud-native architectures, and modern development practices.
• Collaborate with product teams to deliver customer-centric banking solutions.
• Explore new technologies to keep RBC at the forefront of innovation.
• Work with business and senior management to execute the next-gen vision.

Skills

Leadership

Python

React

REST APIs

AI Technologies

Mentoring

CI/CD

Tools

Ansible

Postman

Swagger

LDAP

Vault

Kubernetes

Job description

RBC in Toronto is seeking a Director of Engineering to shape the future of our digital platform by leading embedding models to transform data into vectorized representations, enabling semantic understanding and contextual relevance in AI-driven applications.

You will oversee knowledge graphs integration, drive RAG techniques for real-time data retrieval, and govern LLM deployment while maintaining enterprise compliance.