## About the Role

As a Staff AI FinOps Governance Lead, you will own financial governance for UKG’s AI platforms and services, with a focus on accountability, transparency, and optimization across AI investments. The role exists to help AI innovation scale efficiently and sustainably while maintaining strong financial discipline.

You will work closely with Engineering, Product, Finance, Procurement, and Cloud Operations to define governance, improve AI spend management, and provide executive visibility into the performance of the AI portfolio. The role centers on emerging GenAI cost drivers such as LLMs, inference services, vector databases, GPUs, and AI agent workloads.

## Key Responsibilities

– Lead AI FinOps governance across AI initiatives, including operating rhythms, accountability, and financial transparency.
– Define AI cost KPIs, operational metrics, executive dashboards, and long-range forecasting models.
– Monitor AI usage and spend across cloud providers and AI platforms to identify anomalies and optimization opportunities.
– Drive cost optimization across model selection, inference patterns, caching, prompt optimization, GPU utilization, model routing, and workload placement.
– Establish budgets, alerts, quotas, and governance policies to reduce overruns and strengthen financial control.
– Partner with Cloud Operations, Procurement, and Finance on commitments, provider agreements, and consumption forecasting.
– Ensure accurate cost allocation, tagging, chargeback/showback, and AI unit economics.
– Collaborate with Engineering to embed FinOps principles into AI architecture and platform decisions.
– Build reporting and executive dashboards that turn cloud and AI consumption data into actionable insight.
– Analyze billing and usage data using SQL, Python, Excel, BI tools, and cloud-native reporting tools.
– Develop cost-to-serve models and use AI unit economics to inform product strategy and investment decisions.

## Required Skills

– 7+ years of experience in FinOps, Cloud Financial Management, Cloud Infrastructure, Technical Program Management, or a related discipline.
– Experience managing cloud costs across GCP, AWS, Azure, or multi-cloud environments.
– Experience partnering with engineering organizations on technical and financial optimization initiatives.
– Experience presenting financial insights and recommendations to senior leadership.
– Strong understanding of cloud billing models and FinOps best practices.
– Working knowledge of AI infrastructure, including LLM services, GPU-based workloads, inference platforms, and cloud AI offerings.
– Experience with SQL, Python, Excel, and BI visualization tools.
– Familiarity with cloud-native cost management platforms and FinOps tooling.
– Executive communication and storytelling with data.
– Financial modeling, forecasting, and budgeting.
– AI and cloud cost optimization.
– Program leadership across cross-functional organizations.
– Analytical problem solving and strategic thinking.
– Ability to influence without direct authority.

## Preferred Skills

– FinOps Certified Practitioner or FinOps Certified Professional.
– Experience with Generative AI platforms such as Vertex AI, Azure OpenAI, Amazon Bedrock, Anthropic, OpenAI, or similar services.
– Experience with AI cost optimization techniques such as prompt optimization, model routing, caching, context management, and inference optimization.
– Understanding of AI unit economics, token attribution, AI agent cost models, and retrieval-augmented generation (RAG) architectures.
– Experience with cloud commitment strategies including CUDs, Reserved Instances, Savings Plans, or AI capacity reservations.
– Bachelor’s degree in Engineering, Computer Science, Finance, Mathematics, Business, or a related discipline; MBA preferred.

## Cloud Platforms & Technologies

### Cloud Providers
– Google Cloud Platform (GCP)
– AWS
– Azure
– Multi-cloud environments

### AI & GenAI Platforms
– Vertex AI
– Azure OpenAI
– Amazon Bedrock
– Anthropic
– OpenAI
– LLM services
– Inference services
– Cloud AI offerings

### Data, Analytics, and Reporting
– SQL
– Python
– Excel
– BI visualization tools
– Cloud-native reporting tools

### FinOps and Cost Management
– Cloud-native cost management platforms
– FinOps tooling

### AI Infrastructure
– Vector databases
– GPUs
– GPU-based workloads
– AI agent workloads
– Retrieval-augmented generation (RAG) architectures

## FinOps Responsibilities

– Set AI FinOps governance, operating rhythms, accountability, and financial transparency.
– Build KPIs, dashboards, forecasts, and long-range plans for AI infrastructure and GenAI services.
– Track AI spend and usage across cloud providers and AI platforms to identify anomalies and inefficiencies.
– Optimize spend through model selection, inference patterns, caching, prompt optimization, GPU utilization, model routing, and workload placement.
– Evaluate tradeoffs between reserved capacity, committed use discounts, pay-as-you-go pricing, and emerging AI consumption models.
– Manage budgets, alerts, quotas, cloud commitments, AI provider agreements, tagging, cost allocation, chargeback/showback, and unit economics.
– Present cost trends, optimization progress, forecasts, and investment recommendations to senior leadership.

## Benefits

– Base pay range: $102,300 to $161,755, with final pay based on skills, experience, job-related knowledge, and work location.
– Eligibility for a performance-based bonus plan.
– Eligibility for restricted stock unit awards as part of total compensation.
– Access to UKG benefits and rewards.

## Useful Links

– Company Website: ukg.com
– Benefits: https://www.ukg.com/about-us/careers/benefits

## Why You Might Be Interested

This role sits at the intersection of AI, cloud economics, and cross-functional governance. You will help shape the financial framework that supports AI innovation at scale while improving visibility, accountability, and efficiency. The position offers regular exposure to senior leadership and close partnership with Engineering, Product, Finance, Procurement, and Cloud Operations. For someone with FinOps and cloud financial management experience, it provides a meaningful opportunity to influence how AI investments are planned and optimized.