Principal Manager - Artificial Intelligence

Belonging to Air Canada is akin to belonging to a Canadian symbol, Air Canada recently voted Best Airline in North America. Take your career to new heights by joining our innovative and diverse team at the forefront of passenger air transport.

Air Canada’s Data Science and Artificial Intelligence team designs the machine learning models, optimization engines, and AI agents that support Customer Service, Operations, Commercial Affairs, and Corporate Services functions. The team is distributed across specialized units aligned with the business, each possessing a product portfolio ranging from the initial idea to production and implementation.

As Principal Manager – Artificial Intelligence, the incumbent will lead a portfolio of AI solutions for one or more business domains, including corporate services (Human Resources, Legal Affairs, Information Technology, and Procurement), as well as a service-wide AI engineering practice domain, such as machine learning and machine learning operations, or the design and evaluation of AI agents. This person will be responsible for the delivery and execution of machine learning and AI agent solutions, and ensuring that each model and agent is of measurable reliability prior to launch and maintenance in production.

The chosen candidate will lead a portfolio of AI implementation and workflow teams, directly and through technical managers, including AI systems developers, data scientists, and machine learning operations specialists, and will work in close collaboration with key assigned stakeholders and IT, Architecture, and Cybersecurity departments.

The successful candidate will report to the Director – Data Science and Artificial Intelligence.

Responsibilities :

• Ensure the execution of the AI portfolio of assigned business domains (e.g., HR, Legal Affairs, IT, and Procurement): transform opportunities prioritized by the AI governance process into functional machine learning and AI agent solutions, with clear value milestones agreed upon with assigned stakeholders.
• Define the AI strategy and roadmap for the assigned portfolio and practice domain, in collaboration with the Director – Data Science and Artificial Intelligence.
• Lead and build a team of technical managers, AI systems developers, data scientists, and machine learning operations specialists, taking notably into account objectives, coaching, performance, and career development.
• Handle recruitment, selection, and onboarding, as well as maintaining a high level of employee engagement within the team.
• Manage and allocate the capacity, budget, and external resources necessary for the implementation of the assigned portfolio and practice domain, balancing competing priorities and providing effort and resource estimates to the governance process when new opportunities are evaluated.
• Ensure that solutions in production are monitored, supported, and continuously improved. Communicate their accuracy, adoption, and value to assigned stakeholders and senior management.
• Act as the subject matter expert resource for the service in their area of expertise (machine learning, machine learning operations, AI agents) and advise all teams on approach, architecture, and risks.
• Apply and promote an evaluation-driven design approach: success criteria and evaluation methods are agreed upon with business experts before construction begins, and each model and agent must achieve agreed accuracy and safety thresholds before production deployment.
• Contribute to the definition and updating of the service’s standard stack, reusable templates, and machine learning operations practices so that teams move from prototype to production quickly and consistently.
• Ensure that AI solutions are deployed through continuous integration and deployment pipelines with automated testing and evaluation controls.
• Establish standards for code quality, security, monitoring, and production support across the service.
• Act as the primary technical counterpart of the service with IT, Architecture, Cybersecurity, and other implementation teams, influencing cross-functional priorities and decisions and resolving systemic roadblocks related to environments, access, and platforms.
• Ensure that responsible AI, security, and privacy reviews are conducted in collaboration with Cybersecurity, Legal Affairs, and Privacy, as needed.
• Lead the service community of practice in their area of expertise, anticipate future capacity and skills needs, refine team expertise, and keep tools and practices up to date as the field evolves.

Qualifications

• At least eight years of experience in machine learning, AI systems development, data science, or software engineering, including at least five years in personnel management (hiring, performance, development), and experience in leadership through team leads.
• Bachelor's degree in computer science, engineering, mathematics, or a related discipline. Master's or PhD is an asset.
• Deep expertise in at least one of the following areas, and working knowledge of the following: a) machine learning and machine learning operations, notably building, deploying, monitoring, and retraining machine learning models in production; b) AI solutions and agents based on Large Language Models (LLMs), notably their design, quality and safety evaluation, and production monitoring.
• Experience in defining engineering standards for AI teams (continuous integration and deployment, testing, version control, and production support) on a cloud platform, preferably Azure.
• Proven track record in delivering an AI product portfolio to non-technical stakeholders, through to adoption, within a large complex organization.
• Proven ability to act as a technical authority within teams and work with Architecture, Cybersecurity, and IT teams in a large enterprise.
• Clear communication with both technical audiences and senior management.
• Demonstrated punctuality and reliability to foster the team's overall success in a fast-paced environment.

Desirable Skills

• Azure AI Foundry, AKS, Databricks, or other similar platforms.
• Agent frameworks (e.g., LangGraph) and LLM observability and evaluation tools.
• Optimization or operations research solutions in production.
• Responsible AI and generative AI governance within a regulated or enterprise environment.
• Knowledge of the aviation, transport, or logistics sector, an asset.

Employment Conditions :

Candidates must be eligible to work in the country concerned at the time an offer of employment is presented and are responsible for obtaining necessary work permits, visas, or other authorizations. Proof of eligibility must be provided prior to the start date.

Language Requirements

All things being equal, preference will be given to bilingual candidates.

Diversity and Inclusion

Air Canada is strongly committed to diversity and inclusion and aims to create a healthy, accessible, and rewarding workplace that highlights our employees' unique contribution to our company's success.

As an equal opportunity employer, we encourage diverse applications so that we can build a workforce that is varied and representative of our customers and the communities where we live and serve.

Air Canada thanks all candidates for their interest, however only those selected for an interview will be contacted.