Program Overview

Master of Artificial Intelligence Design & Development

Program Overview

The Master of Artificial Intelligence Design & Development program at Seneca Polytechnic is currently pending ministerial consent and funding approval, with its first start planned for September 2025.

Program Details

• Campus: Newnham
• Duration: 4 Semesters (2 Years)
• Credential Awarded: Master's Degree
• School: School of Software Design & Data Science

About the Proposed Program

The Master of Artificial Intelligence Design & Development program aims to equip graduates with the expertise to excel in the fast-evolving artificial intelligence (AI) landscape, bridging the gap between rigorous theory and real-world business solutions.

Program Highlights

• Internship opportunities totalling 840 hours of work-integrated learning
• Deep, specialized training in AI techniques and technologies
• Provides a greater depth of knowledge tailored to the graduate level
• Significant focus on applied research, tracking developments in the AI and ML field to develop novel solutions to the problems of real organizations
• Flexible delivery options to accommodate on-campus and remote learning

Career Opportunities

When you graduate from this program, you’ll be prepared to explore career options such as:

• AI engineer, manager or project manager
• Data analyst
• Data scientist
• Lead data engineer
• Deep learning engineer
• Machine learning software engineer

Program Description

You will receive advanced training in machine learning (ML), natural language processing, computer vision, machine learning operations (MLOps), ethics in AI and more, preparing you to produce and apply AI technologies across multiple sectors.

You will also benefit from experiential learning opportunities including case analysis, labs, an independent applied research project and eight months of internship experience — totalling 840 hours of real-world experience.

By the time you graduate, you will be able to design and refine high-performing machine learning models; evaluate conventional, auto and real-time machine learning algorithms for different use cases; and generate actionable insights from deep learning models to drive business innovation.