Overview You lead the enterprise FinOps discipline for AI, establishing governance, forecasting, allocation, and reporting across AI platforms and services. You partner with Technology, Engineering, Finance, Procurement, and business leaders to ensure AI investments deliver measurable value with clear financial accountability. This hands-on role builds the methodology, analyzes cost data, and articulates AI economics to senior leadership. You set up a scalable, future-ready model that evolves with pricing and taxonomy changes, enabling cost-aware innovation.
Compensation / Benefits- 401K with company match
- comprehensive insurance package
- paid time off including vacation and sick leave
- employee assistance program
- incentive compensation
- tax-advantaged savings plans
Responsibilities- Establish the enterprise FinOps for AI discipline, including scope, taxonomy, methodology, operating model, governance, controls, and performance measures
- Create an integrated view of AI cost and consumption across hyperscaler services, accelerated compute, AI gateways, models, tokens, agents, copilots, data platforms, and embedded vendor capabilities
- Define actionable unit economics and connect AI spending to products, services, outcomes, and customer value
- Lead budgeting, forecasting, scenario modeling, and variance analysis for AI investments with early warning on consumption trends
- Set allocation, showback, and chargeback standards with tagging and reporting by business unit, application, product, and use case
- Identify and deliver optimization opportunities (model routing, prompt/context efficiency, caching, batching, right-sizing, commitments, retirement of low-value usage)
- Track and validate realized savings and cost avoidance, distinguishing actual results from estimates and recommendations
- Embed cost estimation and guardrails into AI intake and development workflows to manage run costs before build
- Partner with AI and cloud platform teams, engineering, architecture, security, procurement, finance, and business stakeholders to ensure reliable data and governance
- Define reporting and tooling requirements, validate logic, and communicate spend, risks, and opportunities through executive showbacks and planning cycles
Key requirements- Practical FinOps or cloud financial management experience with influence over engineering and business decisions tied to consumption
- Strong understanding of AI pricing and consumption drivers (tokens, inference, context windows, model tiers, accelerated compute, licensing, usage behavior)
- Ability to build a new operating discipline by setting methodology, documenting decisions, and adapting to market changes
- Strong analytical judgment with clear distinction among actuals, forecasts, estimates, opportunities, and recommendations
- Clear, concise communication of cost drivers and trade-offs to engineers, finance partners, and executives
- Results-oriented with high standards for documentation, governance, and auditability
- Experience across budgeting, forecasting, allocation, tagging, showback/chargeback, optimization, and cloud commitment management
- Familiarity with FinOps Framework, TBM taxonomy, or related standards; FinOps or cloud certification preferred
- Proficiency with cost and usage data and reporting tools such as Power BI, Microsoft Fabric, Tableau
- Strong cross-functional collaboration
- Strategic influencing in a matrixed environment
- Effective communication of complex cost drivers to non-technical stakeholders
- FinOps / cloud financial management
- AI pricing and economics (tokens, inference costs, model tiers, accelerated compute)
- Budgeting, forecasting, allocation, tagging, showback, chargeback
