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Data Scientist Data & AI (Applied AI)

Air Canada
Full Timemid
Toronto, Ontario, CAPosted 5 days ago

Role Overview

Air Canada is hiring a mid-level Data Scientist Data & AI (Applied AI). This is a full-time role in Toronto. Part of Air Canada's Security hiring, posted 5 days ago. Full responsibilities, required qualifications, and the apply link are listed in the description below.

Salary Context

Salary is not disclosed in this posting. Market median for Mid-level Security roles is $90k-$130k (based on 115 comparable listings). Many employers share specifics during the interview process or after an initial screen.

Resume Keywords to Include

Make sure these keywords appear in your resume to improve ATS scoring

PythonAWSAzureDockerKubernetesAgileCI/CDAPI

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.

As an Data Scientist (Agentic Engineer) within the Data Science & AI team at Air Canada, you will build, deploy, and operate production-grade agentic AI systems that deliver measurable business value. Working within an agentic squad, you will take designed agent workflows and transform them into scalable, reliable, and secure services across Air Canada's cloud environments (Azure and AWS).

This role focuses on system engineering, deployment, CI/CD, and runtime performance, ensuring that agentic solutions operate reliably at enterprise scale. You will collaborate closely with Data Scientists, who own agent design, to deliver end-to-end production solutions. You will join the Data Science & AI Team, a central group within Air Canada's IT organization, supporting internal business units such as Revenue Management, Network Planning, Operations, Maintenance, Cargo, and customer-facing solutions. All initiatives follow an agile methodology, with 2-to-3-week sprints and incremental releases.

Responsibilities

  • Package agentic AI solutions as containerized services with well-defined APIs.
  • Deploy and operate solutions across cloud platforms (Azure and AWS).
  • Design and maintain CI/CD pipelines for versioning, testing, and releasing agent services.
  • Manage runtime environments using Kubernetes and cloud-native services.
  • Integrate agentic solutions with enterprise data systems, APIs, and external services.
  • Implement and maintain monitoring, logging, tracing, and observability pipelines.
  • Ensure system performance across latency, scalability, reliability, and cost efficiency.
  • Enforce security, authentication, and access control (RBAC, identity integration).
  • Handle constraints from the existing enterprise stack (Azure, AWS, networking, security).
  • Collaborate with Data Scientists to ensure agent designs are production-ready.
  • Troubleshoot and resolve issues in deployed agent systems.
  • Contribute to platform standards and reusable runtime patterns for the agentic squad.

Qualifications

  • 5+ years of experience in software engineering, platform engineering, or ML engineering.
  • Strong hands-on experience with containerization (Docker) and Kubernetes.
  • Proven experience with cloud platforms, specifically Azure and AWS.
  • Strong experience designing and operating CI/CD pipelines.
  • Experience building and operating production API-based services.
  • Familiarity with LLM-based systems and agentic architectures (LangGraph a plus).
  • Hands-on experience with observability tools (logging, tracing, monitoring).
  • Strong programming skills in Python and backend systems.
  • Experience with enterprise security, authentication, and integration constraints.
  • Demonstrate punctuality and dependability to support overall team success in a fast-paced environment.

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.

About Air Canada

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Air Canada

aircanada.com

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1 other open role at Air Canada on TryApplyNow.

Frequently Asked Questions

How do I apply for the Data Scientist Data & AI (Applied AI) position at Air Canada?

Use the Apply button above to submit your application directly to Air Canada. Most applications take less than 5 minutes if your resume and contact details are ready, and you'll be routed to the employer's official application system to finish.

Where is the Data Scientist Data & AI (Applied AI) position at Air Canada located?

This position is based in Toronto. Air Canada has not indicated remote or hybrid options for this role, so candidates should plan for on-site work.

What does a Data Scientist Data & AI (Applied AI) at Air Canada earn?

Air Canada has not disclosed a salary range in this posting. Many employers share specifics later in the interview process; you can also ask during a recruiter screen if compensation transparency is important to you.

When was the Data Scientist Data & AI (Applied AI) role at Air Canada posted?

This role was posted on July 6, 2026 (5 days ago). It's still listed as actively hiring; we re-confirm openings against the source system multiple times per day and remove closed roles.

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