Staff AI FinOps & Tokenomics Lead

About UKG:

At UKG, the work you do matters. The code you ship, the decisions you make, and the care you show a customer all add up to real impact. Today, tens of millions of workers start and end their days with our workforce operating platform helping people get paid, grow in their careers, and shape the future of their industries.

We never stop learning. We challenge the norm, push for better, and celebrate the wins along the way. Here, you'll get flexibility that's real, benefits you can count on, and a team that succeeds together. Because at UKG, your work matters and so do you.

About the Role:

UKG is scaling AI across its products, platforms, and internal workflows. That growth creates a new class of financial and operational questions: how much does an AI capability cost to serve, what drives that cost, how does it change with adoption, and what value does it create for customers and UKG? The Staff AI FinOps & Tokenomics Lead will establish the discipline, data, and operating model to answer those questions and turn the answers into better decisions.

This is a senior individual contributor role at the intersection of Cloud FinOps, AI infrastructure economics, product strategy, engineering, finance, and procurement. You will own the AI FinOps agenda across UKG's AI portfolio, including foundation-model consumption, inference, agents, embeddings, vector and retrieval services, GPUs, data platforms, and supporting cloud infrastructure. You will influence architecture and product decisions from planning through production, ensuring AI innovation can scale with transparent economics and durable controls.

The role is expected to operate as a trusted advisor to senior engineering and product leaders. Success will require more than reporting spend: you will create repeatable mechanisms that help teams choose the right model and architecture, forecast demand, manage commitments and capacity, protect margins, and demonstrate measurable business value.

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What You Will Own:

- The AI FinOps roadmap, and operating rhythm across UKG Engineering, Product, Finance, Procurement, Cloud Operations, and AI platform teams.

- A trusted view of AI consumption, cost drivers, unit economics, forecasts, commitments, and value realization across cloud providers and AI vendors.

- The standards, tooling, and decision frameworks that make AI cost and performance visible before architecture and product decisions are locked in.

- Executive-level recommendations on AI investment, risk, optimization, capacity, pricing, and margin implications.

Key Responsibilities:

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.

AI Cost Management, Tokenomics and Optimization:

- Build and maintain cost models for LLMs, inference, agents, embeddings, vector databases, retrieval-augmented generation, GPUs, data movement, storage, observability, and shared platform services.

- Define tokenomics and cost attribution models that connect requests, tokens, context, tools, model routes, tenants, features, and customers to cost-to-serve.

- Partner with engineering to optimize model selection, routing, prompt and context design, caching, batching, quantization, throughput, utilization, and workload placement.

- 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.

- Identify anomalies, waste, and structural cost drivers; lead remediation through measurable initiatives with accountable owners, milestones, and verified savings or avoidance.

Forecasting, Planning and Commercial Economics:

- Develop bottom-up forecasts for AI consumption using adoption, query volume, tokens, model mix, concurrency, utilization, regional deployment, capacity, and product roadmap assumptions.

- Create scenario models for adoption, model migration, workload growth, pricing changes, capacity constraints, and product or architecture alternatives.

- Partner with Finance and Procurement on budgets, vendor agreements, cloud commitments, capacity reservations, rate structures, credits, and renewal decisions.

- Translate technical assumptions into product unit economics, cost-to-serve, price-to-cost relationships, contribution margin, and investment cases.

- Maintain clear separation between committed, forecast, addressable, identified, and realized savings, with auditable assumptions and ownership.

Governance, Controls and Financial Accountability:

- 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.

Engineering and Product Partnership:

- Serve as the FinOps partner for AI platform and product teams, influencing design choices with timely cost, performance, and scale analysis.

- Create lightweight self-service tools, reference architectures, calculators, and decision guides that allow teams to answer common AI economics questions without waiting for bespoke analysis.

- Support launch and capacity-readiness reviews for AI capabilities, including demand signals, quotas, regional placement, production and non-production usage, and operational headroom.

- Coach engineers, architects, product managers, and finance partners on AI economics and practical cost-aware design.

Data, Analytics and Executive Reporting:

- 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.

- Use SQL, Python, cloud billing data, service telemetry, and BI tools to reconcile usage to invoices and identify actionable trends.

- Define KPI hierarchies and leading indicators that connect consumption to efficiency, quality, reliability, adoption, and business value.

- Deliver concise executive reporting with clear decisions, risks, recommendations, and follow-through—not reporting for its own sake.

Measures of Success:

- Within the first 12 months, this role should establish a measurable AI FinOps capability that demonstrates:

- Reliable, explainable AI cost and usage visibility across the material AI portfolio.

- Forecasts and scenario models trusted by Engineering, Product, Finance, and Procurement for planning and investment decisions.

- Documented improvement in AI unit economics through model, token, infrastructure, capacity, and workload optimization.

- Guardrails and decision checkpoints embedded in the AI delivery lifecycle, with exceptions visible and managed.

- A repeatable executive operating rhythm that turns insights into accountable actions and verified savings, avoidance, or margin improvement.

- Self-service guidance and tools that increase the speed and consistency of AI architecture and product decisions.

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Required Qualifications:

- 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.

Technical and Analytical Skills:

- 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.

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Core Competencies:

- 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.

Preferred Qualifications:

- 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.

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Why this role matters:

AI is becoming a core part of how UKG builds products, serves customers, and operates the business. The Staff AI FinOps & Tokenomics Lead will help UKG scale that investment responsibly by making the economics of AI visible, actionable, and connected to value. You will help teams move faster with better information, protect margins as adoption grows, and ensure every AI dollar is tied to a clear business or customer outcome.

Equal Opportunity Employer

UKG is an equal opportunity employer. We evaluate qualified applicants without regard to race, color, disability, religion, sex, age, national origin, veteran status, genetic information, and other legally protected categories.

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Disability Accommodation in the Application and Interview Process

For individuals with disabilities that need additional assistance at any point in the application and interview process, please email [email protected] .

The pay range for this position is $102,300 to $161,755. The actual base pay offered may vary depending on skills, experience, job-related knowledge and work location. In addition to base pay, employees may be eligible to participate in a performance-based bonus plan and to receive restricted stock unit awards as part of total compensation. Learn more about UKG’s benefits and rewards at https://www.ukg.com/about-us/careers/benefits