AI FinOps Operating Model: Lead UKG's AI FinOps operating model, including ownership, decision support , governance forums, intake, escalation paths, and recurring business reviews. Create a repeatable lifecycle for AI economics: business case, architecture review, forecast, launch readiness, production monitoring, optimization, and post-launch value review. Establish common definitions and measurement standards for AI spend, usage, utilization, cost efficiency, quality, latency, and business value. Build executive narratives that connect AI consumption and operating performance to product adoption, customer outcomes, revenue, gross margin, and investment decisions. Build and maintain cost models for LLMs, inference, agents, embeddings, vector databases, retrieval-augmented generation, GPUs, data movement, storage, observability, and shared platform services. Evaluate cost and performance tradeoffs across hosted APIs, self-hosted models, fine-tuning, inference platforms, reserved capacity, committed-use discounts, and pay-as-you-go consumption. Develop bottom-up forecasts for AI consumption using adoption, query volume, tokens, model mix, concurrency, utilization, regional deployment, capacity, and product roadmap assumptions. Establish AI spend guardrails, budgets, alerts, quotas, approval thresholds, service limits, and exception processes that support innovation without creating unnecessary friction. Define tagging, labeling, hierarchy, allocation, showback, chargeback, and shared-service allocation standards for AI workloads. Partner with platform teams to embed cost controls and FinOps telemetry into AI platforms, developer workflows, architecture reviews, and release processes. Ensure controls cover both direct model consumption and supporting infrastructure, including shared services and costs that are otherwise difficult to attribute. Monitor compliance and control effectiveness, and present risks, decisions, and corrective actions through the established operating rhythm. Support launch and capacity-readiness reviews for AI capabilities, including demand signals, quotas, regional placement, production and non-production usage, and operational headroom. Build the data products and dashboards needed to explain AI spend and performance by provider, model, workload, product, tenant, customer, environment, region, and cost driver. Deliver concise executive reporting with clear decisions, risks, recommendations, and follow-through—not reporting for its own sake. 7+ years of experience in FinOps, cloud financial management, cloud infrastructure economics, technical program leadership, product finance, or a related discipline. Demonstrated experience leading cross-functional initiatives across Engineering, Product, Finance, Procurement, and cloud or platform operations. Experience translating technical consumption drivers into forecasts, budgets, unit economics, investment cases, and executive recommendations. Experience influencing senior stakeholders and delivering outcomes without direct authority. Experience working in a SaaS, enterprise software, platform, or other usage-scaled technology business. Strong understanding of cloud pricing, billing, allocation, commitments, capacity, and FinOps practices across GCP, AWS, Azure, or multi-cloud environments. Working knowledge of generative AI architectures, including LLM APIs, inference, agents, embeddings, RAG, vector databases, GPUs, model gateways, and observability. Ability to analyze token usage, model mix, latency, throughput, utilization, quality, and workload patterns to identify economic tradeoffs. Proficiency with SQL and strong working knowledge of Python, spreadsheets, BI tools, and cloud-native reporting or cost-management platforms. Ability to build practical financial models, forecasts, dashboards, and decision frameworks from incomplete or evolving data. Strategic thinking paired with hands-on execution. Executive communication and data-driven storytelling. Financial modeling, forecasting, and business case development. AI and cloud cost optimization with strong commercial judgment. Systems thinking, structured problem solving, and attention to data quality. Influence, facilitation, and ability to create alignment in a matrixed organization. Bias for measurable outcomes, accountability, and continuous improvement. FinOps Certified Practitioner or FinOps Certified Professional. Experience with Vertex AI, Azure OpenAI, Amazon Bedrock, Anthropic, OpenAI, or similar AI platforms. Experience with AI cost optimization techniques such as model routing, prompt and context optimization, caching, batching, quantization, and inference optimization. Experience with AI token attribution, agent cost models, RAG economics, and product-level AI unit economics. Experience managing CUDs, Reserved Instances, Savings Plans, AI capacity reservations, or comparable commercial constructs. Bachelor's degree in Engineering, Computer Science, Finance, Mathematics, Business, or a related discipline; MBA or equivalent experience preferred.