Lead, AI FinOps & Consumption Governance

New American Funding•,

18h•$120,000 - $150,000•Remote

About The Position

New American Funding is scaling AI across software development, borrower- and loan-officer-facing products, and embedded vendor platforms. AI cost grows non-linearly with usage and is driven far more by model selection and usage patterns than by headcount. We are hiring a Lead, AI FinOps & Consumption Governance to own the financial discipline behind every dollar of NAF's AI spend — making it transparent, governed, and tied to measurable value. This is a single-threaded ownership role with real decision authority — not an advisory or reporting seat. You will set budgets at the platform source, allocate cost to the teams that incur it, and partner with Engineering, Finance, and Analytics to keep spend aligned with ROI.

Requirements

Bachelor's degree in a quantitative or technical field, or equivalent experience.
5+ years in FinOps, cloud or SaaS cost management, or technical finance — including direct ownership of variable, usage-based spend.
Hands-on experience in cloud and SaaS cost and admin consoles (e.g., Google Cloud billing, Anthropic Console, Cursor Organizations, or analogous platforms).
Strong working knowledge of LLM pricing mechanics: token billing, context windows, caching and batching, model price-performance trade-offs, and commitment and discount structures.
Proven forecasting and financial modeling for variable-usage systems.
Strong analytical toolkit: SQL, Python, BI tools, and advanced spreadsheet skills.
Track record embedding cost discipline within engineering and product teams (showback/chargeback, tagging, guardrails).
Excellent communication — able to translate complex cost structures for both technical and executive audiences.

Nice To Haves

Experience in regulated financial services or mortgage.
Familiarity with the economics of AI-assisted developer tooling.
Experience working with cloud billing platforms (Azure, GCP, AWS).
FinOps Foundation certification.
Experience building cost governance frameworks for emerging technologies like AI.

Responsibilities

Own end-to-end visibility and control of all AI spend across NAF: AI-assisted development tools (Cursor, Claude Code / Anthropic Console), managed-cloud AI (Google Cloud Vertex AI / Gemini Enterprise), Azure Foundry, embedded vendor AI (e.g., Braze, Genesys), and the cloud infrastructure consumed by AI workloads.
Set, monitor, and enforce budgets at the platform source — including pooled and seat-based structures such as Cursor Organizations — and establish guardrails, usage alerts, and anomaly detection per platform.
Own seat lifecycle economics: allocation, utilization tracking, and reclamation of idle or low-value seats.
Create attribution where vendors don't expose it. For embedded or bundled AI (e.g., Braze, Genesys) that isn't metered at the token or feature level, define a defensible allocation methodology and press vendors for usage telemetry.
Maintain deep, current fluency in token-pricing mechanics across providers: per-model rates, context-window costs, caching and batch economics, and committed-use and discount structures.
Partner with Engineering and AI Enablement to define and govern model-selection and routing policy, so high-cost models are used where they earn it and defaults stay economical.
Manage commitment strategy — committed-use discounts, enterprise commitments, and reserved-capacity options — against forecast demand.
Build cost-allocation pipelines from platform and billing exports into NAF's data-warehouse and BI environment; implement tagging so spend is attributable to team, business unit, model, and workflow.
Implement showback and chargeback across pods and business units.
Define NAF's AI unit economics (cost per developer, per accepted change, per workflow, per loan or borrower interaction) and work with Finance and Analytics to test spend against value.
Deliver monthly executive reporting and on-demand usage and trend reporting; build forecasts tied to product-rollout milestones.
Implement and maintain FinOps tooling for AI spend in alignment with NAF's existing Finance and FinOps function.
Automate cost pipelines using platform and billing exports, SQL and Python transformations, and BI dashboards.
Standardize metrics and definitions across all platforms and models so reporting is comparable and trusted.
Set and enforce platform- and team-level AI budgets, including reallocation of central reserve capacity.
Own idle-seat reclamation policy and execution.
Co-own default model policy and cost guardrails with AI Enablement, and gate access to high-cost models.
Escalate and, where warranted, apply temporary throttles on anomalous spend.
Own the allocation taxonomy and reporting definitions the organization runs on.