Lead, Enterprise AI FinOps

The Enterprise AI FinOps Lead establishes and operates the financial governance discipline for company-wide AI usage. This roleownscost visibility, show-back, optimization, budgeting, forecasting, and stakeholder engagement across a multi-cloud (Azure, AWS, GCP), SaaS, andon-premiseAI landscape.

This is not a traditional cloud cost role. It requires deep understanding of AI-specific cost drivers—models, tokens, prompts, agents, embeddings, context windows, RAG systems, and adoption behavior—and the ability to translate telemetry into actionable insights, optimization programs, and executive reporting.

The role also owns full budget management for the Enterprise AI department.

This role requires full-time onsite work in San Diego, CA (5 days per week).

**This role is not eligible for Qualcomm immigration sponsorship.**

Key Responsibilities

1. Build & Operate the AI FinOps Function

• Establish theAI-FinOpsoperating model: visibility, cost accountability, show-back, optimization cadences, and leadership reporting.

• Define processes, metrics, and governance mechanisms that manage costs while enabling adoption.

2. AI Cost Visibility & Intelligence

• Own end-to-end usage visibility across all cloud,on-premise, internal, and SaaS AI platforms.

• Identify major cost drivers and flag areas where cost growth outpaces value delivery.

3. Show-Back & Chargeback Readiness

• Design and implement show-back methodology with transparent allocation rules for shared platforms, model gateways, agent platforms, RAG systems, and SaaS AI.

• Deliver monthly/quarterly show-back packages to business unit and functional leaders.

4. Budgeting, Forecasting & Financial Management

• Own AI cost forecasting and budget planning based on usage trends, adoption growth, model pricing, and optimization initiatives.

• Manage departmental budget: planning, tracking, accruals, purchase planning, and vendor cost visibility.

• Partner with Finance, Procurement, Cloud FinOps, Legal, and platform owners to ensure financial discipline.

5. Optimization & Cost Reduction

• Identify and drive cost reduction opportunities without compromising business value or adoption.

• Lead recurring reviews with high-consuming teams to eliminate waste and promote efficient usage.

6. Executive Reporting & Leadership Engagement

• Produce executive-ready AI cost/usage reporting for CIO, Finance, IT, and business unit leaders.

• Develop narratives around cost growth, business value, adoption health, and efficiency gains.

• Define KPIs that distinguish healthy adoption from uncontrolled consumption.

Required Qualifications

• 8+ years in FinOps, cloud cost management, technology finance, IT financial management, or enterprise analytics.

• Proven experience managing budgets, forecasts, financial reporting, and cost optimization programs.

• Strong understanding of cloud cost models (Azure, AWS, or GCP).

• Ability to analyze large-scale usage data and deliver actionable business insights.

• Strong stakeholder management across senior leaders, finance partners, and technical teams.

• Experience building dashboards, KPIs, and recurring operating reviews.

• Excellent executive communication skills (written and verbal).

• Ability to operate independently anddevelop operating models and business processesfrom the ground up.

Preferred Qualifications

• Experience with AI/GenAI/LLM platforms, token-based pricing, or AI platform operations.

• Familiarity with tokens, embeddings, RAG, agents, context windows, model routing, prompt optimization, and inference workloads.

• Multi-cloud cost management experience.

• On-premiseinfrastructure cost allocation experience.

• Cross-functional partnership with Finance, Procurement, Legal, Contracts, and Engineering.

• Proficiency with Power BI, Tableau, Looker, Databricks, Snowflake,BigQuery, SQL, Python, or similar tools.

Ideal Candidate Profile

A practical operator who sits at the intersection of AI platforms, finance, data, and enterprise leadership—equally comfortable analyzing raw usage data and presenting cost trends to senior executives. Treats FinOps not as cost-cutting, but as enabling responsible AI adoption with visibility, accountability, and efficiency.