Staff FinOps AI Governance Lead

• 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.

• 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).

• 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.

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