AI Engineer (FinOps)

TekRek; Toronto; On-site; CAD 90,000 - 120,000; Full time

Job summary

TekRek, in partnership with a financial services organization in Toronto, is expanding its AI and platform engineering capabilities with a focus on governance, spend visibility, and multi-entity cost reporting.

You will drive FinOps for deployed AI workloads, optimize routing, and implement cost controls across a multi-entity environment to help engineering teams use the right models and provide leadership with clear spending insights.

Qualifications

• HHands-on FinOps experience focused on AI/LLM workloads.
• Experience with routing approaches that map workloads to model tiers.
• Practical experience building consumption tracking and cost reporting.

Responsibilities

• Analyze AI and LLM consumption to identify token waste and cheaper model options.
• Build routing logic directing requests to appropriate model tiers by intent.
• Develop usage tracking, cost attribution, and chargeback/showback across entities.
• Collaborate with platform engineering to bake FinOps controls into deployments.
• Improve monitoring of token usage, model consumption, cost trends, and access controls.

Skills

AI/LLM FinOps

Model routing

Cost attribution & showback

Multi-entity reporting

Job description

TekRek has partnered with a financial services organization that is expanding its AI enablement and platform engineering capabilities, with a growing focus on how model usage is measured, governed, and allocated across the business.

As AI adoption increases, the team is putting stronger controls around model consumption, cost attribution, and usage visibility across a multi-entity operating structure. A focus on AI model governance throughout the platform.

The Role

This is a hands-on AI/Data FinOps contract focused specifically on the economics of deployed AI workloads. You will work across model routing, consumption tracking, cost allocation, and governance, helping engineering teams use the right models while giving leadership a clearer view of where AI spend is going.

You should be coming from a strong data engineering and governance background.

What You Will Do

• Analyze AI and LLM consumption to identify unnecessary token usage and cases where lower-cost models can handle the workload effectively.
• Build routing logic that directs requests to an appropriate model tier based on intent, rather than defaulting to higher-cost options.
• Develop usage tracking, cost attribution, and chargeback or showback reporting across separate business entities.
• Work with platform engineering and AI enablement teams to build FinOps controls into model deployment and release workflows.
• Improve monitoring around token usage, model consumption, cost trends, and access controls, including thresholds and guardrails where appropriate.

What You Bring

• Hands-on FinOps experience focused specifically on AI or LLM workloads, including model, token, or compute economics.
• Experience with model gateways or routing approaches that match workloads to different model tiers.
• Practical experience building or operating consumption tracking, cost attribution, chargeback, or showback frameworks.
• Experience working with financial services cost and reporting requirements, including multi-entity or segregated business structures.
• Strong stakeholder communication skills, with the ability to turn technical cost data into clear decisions for engineering and business leaders.

Why This Role

The work sits directly between AI engineering, platform infrastructure, and financial governance. You will have a clear mandate to reduce avoidable model spend, improve how usage is allocated and reported, and put repeatable cost controls around a growing AI deployment environment.