Cloud FinOps and AI Specialist in Toronto

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Air Canada is looking for a Cloud FinOps and AI Specialist to support the financial management, optimization, governance and operation of its growing corporate cloud and artificial intelligence (AI) ecosystem.

This position will focus primarily on artificial intelligence financial operations and cloud financial management, enabling Air Canada to understand, forecast, optimize, and govern its investments in corporate cloud and AI platforms. This includes large language models (LLMs), generative AI applications, AI agents, usage-billed AI services, and enterprise AI licenses.

A key aspect of the role will be to establish visibility into both the costs and business value of AI adoption. The specialist will help develop financial models, forecasts, dashboards, and Return on Investment (ROI) assessments that will enable technology leaders and business executives to make informed decisions regarding AI investments. The incumbent will also participate in the management and operation of the enterprise's AI platforms and provide operational support for certain Development and Operations platforms. The ideal candidate combines strong skills in financial operations, financial analysis, cloud management, and data analysis with a practical understanding of enterprise AI platforms, AWS, generative AI consumption models, license agreements, and platform operations.

Responsibilities:

• Monitor, analyze, and optimize AI-related spend across all enterprise AI platforms and services.
• Track LLM-related token consumption, inference requests, API usage, model usage, licenses, and related infrastructure costs.
• Analyze AI consumption trends across applications, projects, business units, and users to determine optimization opportunities.
• Evaluate the financial effectiveness of AI models using indicators such as cost per token, cost per request, utilization rate, and cost-performance trade-offs.
• Identify opportunities to optimize AI-related spending through wise model selection, usage habit analysis, license management, and consumption control.
• Monitor costs related to AI agents, automated workflows, and multi-modal AI applications.
• Analyze enterprise AI license usage and identify opportunities to improve their allocation and reduce unused or underutilized capacity.
• Collaborate with AI, application development, and other teams to promote cost-effective AI usage practices.

Cloud Cost Management and Optimization

• Monitor and analyze AWS cloud spending by account, application, and business unit.
• Identify cost optimization opportunities, including resource rightsizing, elimination of unused resources, storage optimization, and improved consumption efficiency.
• Analyze AWS consumption habits and formulate actionable recommendations to improve cloud financial efficiency.
• Support the optimization of AWS Savings Plans, Reserved Instances, and other cloud pricing commitments.
• Collaborate with development and operations, site reliability engineering, applications, infrastructure, and cloud teams to implement cost optimization initiatives.
• Track and report on savings, avoided costs, and financial benefits generated by optimization activities.

Budgeting, Forecasting, and Financial Planning

• Support budgetary planning, forecasting, and the management of annual and ongoing cloud and AI expenditures.
• Analyze historical spend, consumption habits, and adoption trends to develop financial projections.
• Develop and maintain forecasts regarding enterprise AI platforms, cloud services, licenses, and related technology investments.
• Monitor actual spending against budgets and forecasts, and analyze significant variances.
• Develop financial dashboards and reports for senior management highlighting spending, consumption trends, financial risks, and optimization opportunities.
• Collaborate with finance and technology stakeholders to improve financial accountability and spend visibility.
• Develop financial models to estimate the cost of new AI use cases, applications, platform adoption, and infrastructure expansion.
• Provide cost forecasts and financial analyses to support cloud and AI investment decisions.

AI Investment Analysis and Return on Investment

• Develop and maintain ROI evaluations for enterprise AI platforms, services, and initiatives.
• Collaborate with Finance, business units, and technology stakeholders to evaluate the operational and financial value generated by AI investments.
• Collaborate with various teams to establish indicators to measure the return on AI investments, including realized savings, productivity gains, operational efficiency gains, utilization rate, and measurable financial benefits.
• Support business cases and financial justification for new AI initiatives and platform investments.
• Compare actual AI spending with anticipated and realized benefits to evaluate investment effectiveness.
• Develop dashboards and reports for senior management providing visibility into AI investment returns.
• Identify underutilized or low-value AI investments and recommend ways to improve financial return.
• Collaborate with designated owners to validate realized benefits and distinguish measurable financial savings from estimated productivity gains.
• Provide financial analyses to help prioritize AI investments and maximize business value.

AI Financial Governance

• Develop and maintain cloud and AI cost dashboards for technical teams, management, Finance, and commercial stakeholders.
• Establish cost allocation and reporting practices by AI application, project, service, and business unit.
• Support showback and chargeback models for cloud and AI consumption.
• Define and monitor key AI financial performance indicators, such as cost per user, application, request, model, and token.
• Implement and manage budgets, spending limits, alerts, and anomaly detection, to the extent permitted by concerned platforms.
• Analyze unforeseen variations in cloud and AI consumption and evaluate their financial impact.
• Support AI financial governance policies, consumption standards, and cost management practices.
• Support cloud resource tagging and cost allocation standards to improve financial accountability.

Licenses, Cost Management, and AI Operations

• Monitor and optimize enterprise AI licenses, subscriptions, and usage-billed services.
• Analyze license usage and adoption across different platforms.
• Support financial evaluations of AI service providers, pricing models, licensing structures, and commercial agreements.
• Collaborate with Procurement, Finance, technology and security teams, and suppliers to support contract renewals and consumption commitments.
• Identify opportunities to improve licensing efficiency and reduce unnecessary spending.
• Provide financial analyses to support decisions related to AI platform renewal, expansion, and licensing.
• Support the implementation of AI security controls, access management, governance, and compliance requirement adherence.
• Coordinate with technology providers and internal stakeholders to continuously improve platform operations.
• Collaborate with architecture, cybersecurity, infrastructure, and engineering teams to meet requirements regarding scalability, reliability, performance, security, and governance.
• Ensure platform administration: configurations, user access, groups, licenses, service availability, and integrations.
• Monitor performance, latency, throughput, token usage, availability, and LLM consumption.
• Support model selection, version management, and platform configuration.

Development and Operations Platform Support

• Provide operational and administrative support for certain enterprise Development and Operations platforms and tools.
• Support platform configuration as well as the management of user access, groups, permissions, and licenses.
• Monitor platform consumption, license usage, and infrastructure costs.
• Collaborate with Development and Operations teams to identify opportunities for platform and license cost optimization.
• Support operational reporting, supplier relations, and platform administration.
• Maintain documentation relative to the platform, operational procedures, and support processes.
• Collaborate with Engineering teams regarding platform requirements and integrations.

Qualifications

• Qualifications (education, certifications and continuing education, experience, good judgment, communication, and contacts) (five to eight bullet points).
• At least five years of relevant experience in Financial Operations, cloud financial management, IT financial management, AI platform operations, cloud platform operations, or a related field.
• Solid experience in financial analysis, budgeting, forecasting, cost management, and cloud or technology service optimization.
• Practical experience in cloud financial management on AWS and cost analysis tools.
• Solid understanding of cloud consumption, pricing, billing, cost allocation, and optimization concepts.
• Understanding of generative AI, large language models, AI consumption models, and usage-billed AI pricing.
• Knowledge of token pricing, inference-related consumption, subscription models, and enterprise AI licenses.
• Experience analyzing large volumes of cloud or technology consumption data and translating findings into actionable recommendations.
• Experience developing financial dashboards, forecasts, cost reports, and financial analyses for senior management.
• Experience in ROI evaluation, business cases, investment analysis, or financial benefit tracking.
• Knowledge of enterprise AI services like Amazon Bedrock, Anthropic Claude, or GitHub Copilot, or comparable platforms.
• Excellent proficiency in Excel and experience with analysis or visualization tools.
• Strong analytical and problem-solving skills, as well as the ability to bridge technical consumption data with financial and business outcomes.
• Strong written and oral communication skills, including the ability to convey financial and technical information to varied audiences.
• Ability to collaborate with teams responsible for finance, procurement, cloud, development and operations, site reliability engineering, data and AI, cybersecurity, architecture, engineering, and business units.
• Leverages financial acumen and analytical mindset to optimize technology investments, forecast costs, and foster data-driven decision making.
• Collaborates effectively with business and technology leaders, exercising influence over stakeholders and communicating analyses to senior management.
• Promotes operational excellence through strong problem-solving skills, attention to detail, accountability, and a commitment to continuous improvement.
• Demonstrates punctuality and reliability to support the overall team success in a fast-paced environment.

Assets

• FinOps Foundation Certification (FinOps Certified Practitioner or FinOps Certified Professional), or equivalent experience.
• AWS Certification (for example, AWS Certified Cloud Practitioner or AWS Certified Solutions Architect).
• Experience managing AI-related spend within a large enterprise.
• Experience in enterprise licensing, administration, or usage analysis of Anthropic Claude.
• Experience in administration, licensing, seat utilization, or adoption analysis of GitHub Copilot.
• Knowledge of pricing, model usage, inference-related consumption, and cost management of Amazon Bedrock.
• Understanding of AI agents, agentic workflows, AI gateways, and multi-modal architectures.
• Experience with AWS Organizations, consolidated billing, Savings Plans, and Reserved Instances.
• Experience with AWS Cost Explorer, AWS Budgets, Cost and Usage Reports, Cost Anomaly Detection, Amazon Athena, SQL, or similar analysis tools.
• Experience implementing cloud financial governance models, showback, or chargeback.
• Experience measuring productivity gains, operational efficiency gains, or financial returns related to AI.
• Knowledge of GitHub, Harness, integration and delivery concepts, and enterprise Development and Operations platforms.
• Knowledge of Python, Terraform, infrastructure as code, or automation concepts.

Employment Conditions:

Candidates must be eligible to work in the country concerned at the time a job offer is presented and are responsible for obtaining work permits, visas, or other necessary 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 work environment that showcases the unique contribution of our employees to our company's success.

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

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

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