Director AI Engineering Operations

Job Title:
Director, AI Engineering Operations & Data Engineering

Location:
Toronto, Canada

Onsite:
Hybrid

Must be authorized to work in Canada
: No sponsorship provided

Pay Rate:
$ k CAD Annual Salary.

About the Role

This role oversees the strategic direction and execution of both our core Data Engineering & Integrations function and a newly formed AI Engineering Operations function. You'll ensure the scalable, reliable, and efficient flow of data and the seamless deployment and operation of our AI models, serving as a key partner to our internal business stakeholders.

This role requires a leader with deep expertise in modern data architectures/frameworks, cloud-native platforms (AWS, Snowflake, Databricks, etc.), and the emerging field of MLOps/AIOps. You must be adept at building and managing high-performing engineering teams, driving complex technical roadmaps, and building both technical and business relationships across the organization.

Job Duties:

Leadership & Strategy

• Define and champion the vision, strategy, and roadmap for Data Engineering, Integrations, and AI
  Engineering Operations.
• Lead, mentor, and grow a diverse team of data engineers, integration specialists, and AI/MLOps engineers, fostering a culture of innovation, reliability, and ownership.
• Partner with the VP of EDATA and other EDATA Directors (Data Platforms, Data SRE, AI Strategy, etc.) to ensure a cohesive and well-governed enterprise data and AI ecosystem.

Data Engineering & Integrations

• Oversee the design, development, and maintenance of robust, scalable, and high-performance ETL/ELT data pipelines utilizing platforms like Snowflake and Databricks.
• Ensure data quality, integrity, and security standards are strictly enforced within all data pipelines and integrations.
• Manage the strategy and execution of all enterprise data integrations, connecting core business systems (e.g., Sales, Finance, HR) to the central data platforms.

AI Engineering Operations (AIOps/MLOps)

• Establish and lead the new AI Engineering Operations function, defining its processes, best practices, and technology stack.
• Implement and manage the MLOps lifecycle, including model training orchestration, continuous integration/continuous deployment (CI/CD) for models, and automated testing.
• Design and provision the production environment for AI models, ensuring scalability, low-latency inference, and seamless integration with end-user applications.
• Collaborate with Data Platforms, Data Science & Innovation and AI Architecture to industrialize experimental models into reliable, production-ready services.

Operational Excellence

• Work closely with Data SRE (Change Management, QA, Monitoring) to implement best practices for pipeline and model observability, alerting, and incident response.
• Ensure the team adheres to architectural standards (defined by the Data & Analytics Architecture team) and security policies (defined by the Data & Analytics Security Architecture team).
• Manage project portfolios, resource allocation, and budget for both Data Engineering and AI Engineering Operations.

Basic Qualifications:

• 10+ years of experience in data engineering, software engineering, or a related technical field.
• 10+ years of expertise with major cloud platforms (AWS, Snowflake, Databricks) and their ecosystems.
• 7+ years of experience managing and leading high-performing engineering teams, including managers.
• Proven experience in designing and scaling complex, enterprise-level ETL/ELT pipelines.
• Experience building and leading an MLOps/AI Engineering Operations function.
• Deep familiarity with MLOps tools and methodologies (e.g., MLflow, Kubeflow, Sagemaker/Azure ML/GCP Vertex AI equivalents).
• Strong understanding of Data and AI Architecture principles and a commitment to secure, governed data practices.
• Proven track record of developing and implementing data engineering and AI Engineering Ops strategies and frameworks in a corporate environment.

Other Qualifications:

• Excellent communication and presentation skills, with the ability to articulate complex technical concepts to non-technical stakeholders.
• Leadership and team management experience, with the ability to inspire and develop talent.
• Experience working with cross-functional teams and managing complex projects.
• Deep understanding of AI technologies, machine learning models, and data analytics.
• Strong strategic thinking and problem-solving skills.
• Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Business Administration, or a related field. PhD is a plus