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Technical Account Manager
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Senior Software Developer
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Chief Backend Developer
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Air Canada

Senior Manager, AI Solutions

Toronto, ON, Canada
Dorval, QC, Canada

Full-time

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Job details

Job type: Full-time
Apply by: Nov 16, 2026
Posting language: English
Posted: Nov 2, 2026

Original job description

Being part of Air Canada is to become part of an iconic Canadian symbol, recently ranked the best Airline in North America. Let your career take flight by joining our diverse and vibrant team at the leading edge of passenger aviation.

Air Canada’s Data Science & AI team builds the machine learning models, optimization engines, and AI agents that support the airline’s customer care, operations, commercial, and corporate functions. The team works in business-aligned pods, each owning a portfolio of products from first idea through production and run.

As Senior Manager, AI Solutions, you will lead a portfolio of AI solutions for one or more business domains, such as the corporate functions (HR, Legal, IT, and Procurement), and a department-wide area of AI engineering practice, such as machine learning and MLOps or the design and evaluation of AI agents. You will be accountable for delivering and running machine learning and AI agent solutions, and for making sure every model and agent is measurably reliable before release and stays that way in production.

You will lead a portfolio of AI delivery teams and workstreams, directly and through Tech Leads, comprising AI developers, data scientists, and MLOps specialists, and work closely with senior business owners and with IT, Architecture, and Cybersecurity.

In this role, you will be reporting to the Director, Data Science & AI.

Responsibilities:

Be accountable for delivering the AI portfolio of the business domains assigned (for example, HR, Legal, IT, and Procurement): turn the opportunities prioritized by the AI governance process into working machine learning and AI agent solutions, with clear value milestones agreed with business owners.
Set the AI strategy and roadmap for the assigned portfolio and practice area, in alignment with the Director, Data Science & AI.
Lead and develop a team of Tech Leads, AI developers, data scientists, and MLOps specialists, including objectives, coaching, performance, and career development.
Responsible for recruitment, selection, and onboarding, and for maintaining a high level of employee engagement within the team.
Manage and allocate capacity, budget, and external delivery resources across the assigned portfolio and practice area, balancing competing priorities and providing effort and resource estimates to the governance process when new opportunities are assessed.
Ensure solutions in production are monitored, supported, and continuously improved, and report accuracy, adoption, and value to business owners and senior leaders.
Act as a department reference in your area of expertise (machine learning and MLOps, or AI agents), advising all pods on approach, architecture, and risk.
Apply and promote an evaluation-led design approach: success criteria and evaluation methods are agreed with business experts before build starts, and every model and agent must meet agreed accuracy and safety thresholds before release.
Contribute to defining and maintaining the department’s standard stack, reusable templates, and MLOps practices so teams move from prototype to production quickly and consistently.
Ensure AI solutions are released through CI/CD pipelines with automated testing and evaluation gates.
Set standards for code quality, security, monitoring, and production support across the department.
Act as the department’s lead technical counterpart with IT, Architecture, Cybersecurity, and other delivery teams, influencing cross-functional priorities and decisions and resolving systemic blockers related to environments, access, and platforms.
Ensure responsible AI, security, and privacy reviews are performed with Cybersecurity, Legal, and Privacy, as needed.
Lead the department’s Community of Practice in your area of expertise, anticipate future capability and skill needs, grow expertise across teams, and keep tools and practices current as the field evolves.

8+ years in machine learning, AI development, data science, or software engineering, including 5+ years managing people (hiring, performance, development), with experience leading through team leads.
Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field. Master’s or PhD is an asset.
Deep expertise in at least one of the following, and working knowledge of the other: (a) machine learning and MLOps, including building, deploying, monitoring, and retraining ML models in production; (b) LLM-based solutions and AI agents, including their design, evaluation of quality and safety, and production monitoring.
Experience setting engineering standards for AI teams (CI/CD, testing, release management, and production support) on a cloud platform, preferably Azure.
A track record of delivering a portfolio of AI products with non-technical business owners, through to adoption, in a large, complex organization.
Proven ability to act as a technical authority across teams and to work with Architecture, Cybersecurity, and IT in a large enterprise.
Clear communicator with technical and executive audiences.
Demonstrate punctuality and dependability to support overall team success in a fast-paced environment.

Preferred Qualifications

Azure AI Foundry, AKS, Databricks, or similar platforms.
Agent frameworks (e.g., LangGraph) and LLM observability and evaluation tools.
Optimization or operations research solutions in production.
Responsible AI and governance for generative AI in a regulated or large enterprise.
Familiarity with the aviation, transportation, or logistics industry is a plus.

Conditions of Employment:

Candidates must be eligible to work in the country of interest at the time any offer of employment is made and are responsible for obtaining any required work permits, visas, or other authorizations necessary for employment. Prior to their start date, candidates will also need to provide proof of their eligibility to work in the country of interest.

Linguistic Requirements

Based on equal qualifications, 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 work environment which highlights employees’ unique contributions to our company’s success.

As an equal opportunity employer, we welcome applications from all to help us build a diverse workforce which reflects the diversity of our customers, and communities, in which we live and serve.

Air Canada thanks all candidates for their interest; however only those selected to continue in the process will be contacted.

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More about this job

Responsibilities

The Senior Manager will lead a portfolio of AI solutions and engineering practices, overseeing the end-to-end delivery of machine learning models and AI agents. They are responsible for managing cross-functional teams, setting technical strategy, and ensuring all solutions meet reliability and safety standards.

Requirements

Candidates must have over 8 years of experience in machine learning, AI development, or software engineering, with at least 5 years in a management role. A bachelor's degree in a technical field is required, along with deep expertise in MLOps or LLM-based solutions.

Skills

Machine Learning
AI Engineering
MLOps
AI Agents
Team Leadership
Strategy Development
Cloud Platforms
Azure
CI/CD
Data Science
Software Engineering
Product Management
Stakeholder Management
Architecture
Cybersecurity
Governance

Visa sponsorship

Not detected in the job text

Keywords

Machine Learning
AI Solutions
MLOps
AI Agents
LLM
Azure
Databricks
AKS
CI/CD
Data Science
Software Engineering
Optimization
Operations Research
Governance
Cloud Computing
Architecture
Cybersecurity
LangGraph
Product Lifecycle
Technical Leadership
Aviation
Logistics
Responsible AI
Automation
Performance Management
Strategic Planning
Evaluation-led Design
Production Support
Stakeholder Engagement
Enterprise Systems

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