Staff AI FinOps Governance Lead

About the role

The Staff AI FinOps Governance Lead at UKG is responsible for leading financial governance for UKG's AI platforms and services. This role involves combining expertise in cloud financial management, AI infrastructure economics, and cross-functional program leadership to ensure efficient and sustainable AI innovation.

This role requires someone who understands both cloud economics and the emerging cost drivers of Generative AI, including LLMs, inference services, vector databases, GPUs, and AI agent workloads.

Key Responsibilities

• AI FinOps Governance & Strategy Lead
• AI FinOps governance across UKG AI initiatives, establishing operating rhythms, accountability, and financial transparency.
• Define AI cost KPIs, operational metrics, and executive dashboards that measure efficiency, utilization, and business value.
• Develop forecasting models and long-range financial plans for AI infrastructure and GenAI services.
• Present AI cost trends, optimization progress, forecasts, and investment recommendations to senior leadership.
• AI Cost Management & Optimization
• Monitor AI usage and spend across cloud providers and AI platforms, identifying anomalies, inefficiencies, and optimization opportunities.
• Drive measurable cost optimization initiatives across model selection, inference patterns, caching strategies, prompt optimization, and infrastructure utilization.
• Evaluate tradeoffs between reserved capacity, committed use discounts, pay-as-you-go pricing, and emerging AI consumption models.
• Partner with Engineering to optimize token consumption, GPU utilization, model routing, and workload placement.
• Governance & Financial Controls
• Establish AI spend guardrails including budgets, alerts, quotas, and financial governance policies.
• Partner with Cloud Operations, Procurement, and Finance to manage cloud commitments, AI provider agreements, and consumption forecasting.
• Ensure accurate cost allocation, tagging, chargeback/showback, and unit economics across AI services.
• Design governance processes that proactively prevent cost overruns and improve financial accountability.
• Engineering Partnership
• Collaborate with engineering teams to incorporate FinOps principles into AI architecture and platform design.
• Influence technical decisions by providing cost transparency and recommendations throughout the software development lifecycle.
• Support engineering teams with dashboards, training, financial insights, and optimization guidance.
• Develop cost-to-serve models and AI unit economics that inform product strategy and investment decisions.
• Reporting & Analytics
• Build executive dashboards and reporting that translate complex cloud and AI consumption data into actionable business insights.
• Analyze cloud billing, AI usage, and infrastructure metrics using SQL, Python, BI platforms, and cloud-native reporting tools.
• Identify trends, forecast future consumption, and quantify the financial impact of optimization initiatives.

Qualifications

• 7+ years of experience in FinOps, Cloud Financial Management, Cloud Infrastructure, Technical Program Management, or related disciplines.
• Experience managing cloud costs across GCP, AWS, Azure, or multi-cloud environments.
• Experience partnering with engineering organizations to drive 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 Qualifications

• FinOps Certified Practitioner or FinOps Certified Professional.
• Experience with Generative AI platforms including Vertex AI, Azure OpenAI, Amazon Bedrock, Anthropic, OpenAI, or similar services.
• Experience with AI cost optimization techniques including 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).