Staff FinOps AI Governance Lead

ServiceNow•Santa Clara, CA

11h•$176,100 - $308,200•Hybrid

About The Position

Join FinOps Governance team that is part of the Global Cloud Services organization. You’ll apply your expertise in AI cost management and data-driven optimization to lead programs that bring accountability and control to ServiceNow’s AI investments. Working closely with engineering teams building AI-powered skills and experiences, cloud finance leadership, and provider relationships with Azure, AWS, GCP, Anthropic and OpenAI, you’ll be the connective tissue between technical usage and financial discipline. We are looking for a Staff FinOps AI Governance Lead to drive financial accountability and optimization across ServiceNow’s AI spend. This senior individual contributor role combines deep AI infrastructure literacy, data-driven governance, and cross-functional program leadership to ensure our AI investments are managed with the same rigor as our cloud infrastructure. The ideal candidate understands the economics of LLMs as well as they understand engineering and will thrive operating independently while engaging VP-level stakeholders with confidence and clarity.

Requirements

Strong working knowledge of LLM pricing models, multi-cloud AI services (AWS Bedrock, Azure OpenAI, GCP Vertex AI), and AI cost optimization levers.
Proficiency in data analysis using SQL and/or Python to build KPI frameworks, identify usage anomalies, and communicate findings through data visualization.
Hands-on experience with multi-cloud cost governance across AWS, Azure, and GCP, including budget management, alerting, tagging, and billing API familiarity.
Proven cross-functional collaboration skills with engineering teams—ability to communicate cost impact, influence technical decisions, and drive efficiency improvements.
Outstanding communication and executive presentation skills; comfortable preparing and delivering briefings to VP-level audiences on complex technical and financial topics.
Self-directed and highly organized, with the ability to manage multiple concurrent workstreams independently in a fast-paced environment.
Bachelor’s degree in a quantitative field (Computer Science, Engineering, Finance, Mathematics, or related).

Nice To Haves

Experience with AI optimization techniques: model routing strategies, prompt engineering, context length management, batch inference, and caching.
Familiarity with provider-level budget and commitment tools: Azure Cost Management, AWS Budgets, GCP Billing controls, and provider credit management.
Understanding of agentic AI cost patterns including per-agent token attribution, multi-agent cost multiplication, and RAG workflow cost implications.
Experience with GenAI gateway or LLM proxy platforms for token metering, rate limiting, and cost attribution.
Prior involvement with FinOps Foundation working groups or industry communities on AI cost management standards.

Responsibilities

Define, track, and systematically review AI cost and usage KPIs; identify anomalies and outliers that signal potential waste, misuse, or optimization opportunities.
Design and operate an anomaly detection framework to surface suspiciously high AI usage across models and skill teams, and engage engineering collaboratively to investigate and remediate.
Quantify, prioritize, and propose cost optimization opportunities—evaluating levers such as PTU vs. pay-as-you-go trade-offs, model tiering, context reduction, and caching—and drive them to measurable outcomes.
Design preventive governance controls so that cost anomalies and overruns, once identified, are systematically prevented from recurring.
Define and implement AI spend guardrails in coordination with cloud and LLM providers: set up budgets, configure alerts, manage commitment structures, and ensure contractual rate accuracy.
Coordinate the AI FinOps governance program across engineering, finance, and cloud provider relationships—maintaining a clear operating model with documented standards and a regular review cadence.
Prepare and deliver VP-level reporting and presentations on AI cost trends, optimization progress, and forward-looking forecasts, translating technical data into clear financial narratives.
Operate as a self-starter and self-sufficient program owner: define scope, manage stakeholders, and drive workstreams to completion with minimal direction.