Specialist, Data and AI - AI Platforms

Air Canada•Toronto, ON

17h

About The Position

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. The Specialist, Data & Analytics – AI Platforms acts as a technical steward within Air Canada’s enterprise AI ecosystem. The role is accountable for ensuring that AI workloads, platforms, and integration points are deployed, operated, and governed in a manner that aligns with enterprise architecture, security, and operational standards. This position bridges business needs and technical execution by supporting the lifecycle of AI products and ensuring their readiness and reliability across environments.

Requirements

University degree or technical certification in Engineering, Computer Science, Mathematics, Statistics, or other related technical fields.
5+ years of IT experience in large enterprise environments.
Experience with technologies supporting AI/ML environments, including: Azure DevOps & GitHub, Azure Kubernetes Service, Databricks, Azure Machine Learning, Azure AI Foundry, Azure Data Factory, Azure Function Apps, Azure Storage Accounts, Key Vault, SQL Server Databases, Service Bus.
Excellent communication skills (written and verbal).
Strong problem solving and analytical abilities.
Proven capability to work effectively in team driven, collaborative environments.
Demonstrate punctuality and dependability to support overall team success in a fast-paced environment.
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.

Nice To Haves

Experience in airline data management is considered an asset

Responsibilities

Integrate AI products, machine learning models, and data pipelines into enterprise systems, ensuring they are stable, scalable, and aligned with reference architecture.
Configure and maintain connectivity between AI platforms (such as Databricks or Dataiku) and upstream/downstream systems.
Support the onboarding of new AI products into shared platforms and contribute to continuous improvement of integration processes.
Deploy, configure, and maintain AI workloads within Microsoft Azure, adhering to enterprise cloud standards.
Manage compute, storage, networking, and other Azure resources needed for AI platforms.
Contribute to environment governance by applying deployment patterns and enforcing development/test/production separation.
Support infrastructure as code processes using approved templates and CI/CD pipelines.
Implement and maintain CI/CD pipelines for models, inference services, and data workflows in accordance with defined MLOps standards.
Support model packaging, deployment, versioning, rollbacks, and promotion across environments.
Integrate monitoring, observability, and alerting tools into AI workloads to ensure operational health.
Contribute to automated testing and quality checks to improve deployment reliability.
Ensure AI solutions meet operational readiness standards prior to production release, including documentation, support models, and validation checks.
Troubleshoot issues related to AI platforms, integration components, and deployment pipelines.
Perform maintenance activities such as patching, upgrades, and dependency updates.
Develop and maintain runbooks, operational documentation, and knowledge base materials.
Implement identity, access, and security controls defined by Cybersecurity and Architecture teams.
Support secure integration of AI platforms with enterprise data, identity, and governance services.
Apply compliance, audit, and governance requirements across AI environments.
Collaborate with AI Product Managers to support release planning and execution needs.
Partner with Data Scientists to operationalize models and ensure deployment alignment.
Engage with Enterprise Architects and Cloud/Security teams to implement approved designs.
Act as a technical liaison between design teams and operational teams to support production transitions.