## About the Role

The Staff AI FinOps Governance Lead will own financial governance for UKG’s AI platforms and services. The role exists to improve accountability, transparency, and optimization across AI investment while supporting efficient, sustainable delivery of AI capabilities.

You will work closely with Engineering, Product, Finance, Procurement, and Cloud Operations to build governance, manage spend, and provide executive visibility into the AI portfolio. The scope includes emerging generative AI cost drivers such as LLMs, inference services, vector databases, GPUs, and AI agent workloads.

## Key Responsibilities

– **AI FinOps governance and strategy:** Establish operating rhythms, accountability, and financial transparency across AI initiatives.
– **KPI and planning:** Define AI cost KPIs, operational metrics, executive dashboards, forecasting models, and long-range financial plans.
– **Cost monitoring and optimization:** Track AI usage and spend across cloud providers and AI platforms; identify anomalies, inefficiencies, and optimization opportunities.
– **AI spend controls:** Set budgets, alerts, quotas, and governance policies to prevent overruns and strengthen financial discipline.
– **Cross-functional partnership:** Work with Engineering, Cloud Operations, Procurement, and Finance on commitments, provider agreements, and consumption forecasting.
– **Engineering enablement:** Help teams apply FinOps principles to AI architecture and platform design, and support them with dashboards, training, and financial guidance.
– **Allocation and economics:** Ensure accurate cost allocation, tagging, chargeback/showback, unit economics, and cost-to-serve models.
– **Reporting and leadership updates:** Build executive reporting, analyze usage and billing data, and present trends, forecasts, and investment recommendations to senior leadership.

## 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 Vertex AI, Azure OpenAI, Amazon Bedrock, Anthropic, OpenAI, or similar GenAI platforms.
– 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 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 field; MBA preferred.

## Cloud Platforms & Technologies

– **Cloud Providers:** GCP, AWS, Azure
– **AI / GenAI Platforms:** Vertex AI, Azure OpenAI, Amazon Bedrock, Anthropic, OpenAI, LLM services
– **AI Infrastructure & Workloads:** GPU-based workloads, inference platforms, vector databases, AI agent workloads, retrieval-augmented generation (RAG) architectures
– **Data & Analytics:** SQL, Python, Excel, BI visualization tools, cloud-native reporting tools
– **FinOps / Cost Management:** cloud-native cost management platforms, FinOps tooling
– **Commitment Models:** CUDs, Reserved Instances, Savings Plans, AI capacity reservations

## FinOps Responsibilities

– Establish governance for AI spend, including budgets, alerts, quotas, and policies.
– Monitor cloud and AI consumption for anomalies, inefficiencies, and optimization opportunities.
– Build KPIs, dashboards, forecasts, and long-range plans for AI infrastructure and GenAI services.
– Drive optimization across model selection, inference patterns, caching, prompt optimization, GPU utilization, model routing, and workload placement.
– Manage cloud commitments, AI provider agreements, consumption forecasting, cost allocation, tagging, chargeback/showback, and unit economics.
– Present optimization progress, cost trends, forecasts, and investment recommendations to senior leadership.

## Benefits

– Base salary range: $102,300 to $161,755.
– Actual base pay may vary based on skills, experience, job-related knowledge, and work location.
– Eligibility for a performance-based bonus plan.
– Eligibility for restricted stock unit awards.

## Useful Links

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

## Why You Might Be Interested

This role offers the opportunity to shape the financial governance framework behind UKG’s AI portfolio as it scales. You will work across Engineering, Product, Finance, Procurement, and Cloud Operations, with direct exposure to executive reporting and investment decisions. The scope includes emerging AI cost drivers such as LLMs, inference services, vector databases, GPUs, and AI agent workloads. It may appeal to candidates who want to combine FinOps, cloud economics, and cross-functional leadership in a high-impact role.