Read the posting closely and it describes three distinct bodies of work, not one job.

There is a pre-investment layer: building the financial case that lets AI programs get funded in the first place. There is a build layer: standing up the operating model, governance, and unit economics that make AI spend accountable. And there is an ongoing layer: running the function month over month once workloads are live.

Most organizations write this as one job description because they are hiring one person. But the work itself separates cleanly, and it maps to how FinOptik engages.

A large enterprise organization is seeking a Lead Director, AI FinOps to build and scale the enterprise AI FinOps function. This is a high-impact, senior individual contributor role and a true greenfield opportunity for a seasoned leader to shape how AI investments are governed, measured, and optimized across the enterprise.

In this role, you will define the strategy, operating model, governance framework, and tooling required to deliver financial transparency, accountability, and continuous optimization across a rapidly expanding AI ecosystem. This portfolio spans foundation models, fine-tuning, inference workloads, and AI platform services deployed across multiple cloud and on-prem environments.

The Lead Director, AI FinOps will serve as a strategic partner to Engineering, Finance, Product, and Executive Leadership, translating complex AI cost dynamics into clear, decision-ready insights. You will help ensure AI investments are financially disciplined, outcome-driven, and aligned to business priorities, enabling leaders to confidently scale AI innovation while maintaining strong financial stewardship.

This role is ideal for a leader who thrives at the intersection of AI, cloud economics, and enterprise finance, and who is motivated by building new capabilities that directly influence business strategy, innovation velocity, and long-term value creation.

What We Expect Of You

• Design and build the enterprise AI FinOps program from the ground up, including establishing operating models, governance frameworks, and adoption roadmaps across Engineering, Finance, and Product organizations.

• Develop and operationalize AI cost governance processes, including tagging and allocation policies, anomaly detection, budget controls, and automated reporting pipelines for model training, fine-tuning, and inference workloads.

• Design and implement chargeback and showback models that accurately allocate AI spend to business units, products, and teams, ensuring cost accountability at every level of the organization.

• Lead the evaluation, selection, and implementation of AI FinOps tooling across major cloud platforms (AWS, Azure, GCP) and third-party AI providers (e.g., OpenAI, Anthropic, Cohere), ensuring comprehensive visibility into all AI spend.

• Deliver executive-level financial reporting and insights, translating complex AI cost data and unit economics (cost-per-inference, cost-per-token, GPU/TPU utilization) into clear, decision-ready narratives for C-suite and board audiences.

• Partner cross-functionally with Engineering, Procurement, and Finance leadership to drive continuous AI spend optimization, identifying and executing on savings opportunities without compromising model performance or business outcomes.

• Establish and lead an AI FinOps Center of Excellence (CoE), defining roles, processes, and scalable governance policies; serve as the internal subject matter expert and evangelist for AI financial accountability across the enterprise.

Required Qualifications

• 10+ years of experience in FinOps, cloud financial management, technology finance, or related discipline within a large-scale enterprise environment.

• 5+ years in building a FinOps program from inception, including defining operating models, governance frameworks, and enterprise adoption.

• 2+ years in AI cost governance, including cost attribution, forecasting, financial controls, and optimization for model training, tuning, and inference workloads.

• 5+ years in designing and operationalizing end-to-end FinOps tooling and processes, including tagging and allocation standards, anomaly detection, automated reporting, and continuous optimization.

• 2+ years of experience in implementing chargeback and showback models that fairly and auditably allocate AI spend to business units, products, or teams. 2+ years of experience in delivering executive-level financial insights, translating complex AI cost drivers into decision-ready narratives for senior leaders.

• 2+ years of experience in AI services (e.g., AWS Bedrock, Azure AI, Vertex AI), including native cost management capabilities.

• 2+ years of experience in AI workload unit economics, including cost-per-inference, cost-per-token, and GPU/TPU utilization optimization.

Preferred Qualifications

• Experience managing LLM and API-based AI spend (e.g., OpenAI, Anthropic, Cohere), as well as self-hosted model infrastructure.

• Experience establishing a FinOps and/or AI Center of Excellence (CoE), including scalable operating models, roles, processes, and governance.

• Experience in regulated environments, with exposure to audit, compliance, and cost allocation requirements.

• Experience developing executive-level financial dashboards using BI tools such as Tableau, Power BI, or Looker.

• Strong analytical and automation skills using SQL, Python, or similar tools for financial analysis.

• Excellent stakeholder management and communication skills, with the ability to influence without authority across Engineering, Finance, and Product teams.