We are seeking a highly technical FinOps leader to own cost architecture, optimization, and financial observability across our AI and LLM platforms. This role will operate at the intersection ML engineering, cloud infrastructure and finance, with deep involvement in model selection, inference optimization, GPU utilization, and provisioned throughput strategy.

You will partner closely with Engineering, AI/ML Platform, and Finance teams to implement reporting frameworks that enable informed decision-making, optimize resource allocation, and establish sustainable cost models.

You will build cost transparency into the AI stack itself — from token-level economics through GPU cluster utilization — and partner directly with engineering teams to design for cost-efficiency at scale.

AI costs scale non-linearly with usage. As we expand our LLM-powered products, disciplined financial management, throughput optimization, and transparent reporting will be critical to ensuring sustainable growth.

-Develop and maintain dashboards/cost models for all AI/LLM-related infrastructure.

-Implement chargeback/showback models across business units.

-Build cost allocation pipelines integrating cloud billing exports into internal data warehouses.

-Oversight of LLM-related spend (API usage, hosted models, self-hosted models, inference endpoints).

-Help define unit economics for AI usage (cost per request, per workflow, per customer, etc.).

-Deliver monthly executive reporting with actionable insights.

-Develop forecasting models tied to product adoption and growth.

-Vendor Coordination

-Optimize usage of provisioned throughput across all providers.

-Forecast demand and align capacity planning with engineering roadmaps.

-Analyze idle capacity, overprovisioning, and burst patterns.

-Evaluate trade-offs between on-demand vs. reserved capacity vs. self-hosted models.

-Partner with Engineering and CTO to right-size model selection and inference configurations.

-Identify cost-saving opportunities through working with the AI Infrastructure teams

-Work to balance latency, quality, and cost.

-Monitor and report on cost anomalies and usage spikes.

-Determine effective cost per inference

-Implement/manage FinOps tooling for AI/LLM’s in alignment with current FinOps team resources

-Build automated cost pipelines using:

-Cloud billing exports (AWS CUR, Azure Cost Management, GCP Billing)

-SQL / Python-based transformations

-BI tools (e.g., QlikSense)

-Help build automated tagging and allocation frameworks.

-Establish anomaly detection and spend guardrails.

-Standardize metrics across multi-cloud and multi-model environments.

-Integrate cost telemetry into existing tooling.

-5+ years in FinOps, cloud financial management, or technical finance.

-Direct experience managing cloud infrastructure spend (AWS, Azure, GCP).

-Experience with Azure OpenAI, OpenAI API, Anthropic, or similar platform consoles.

-Experience working with AI/ML or LLM-based workloads.

-Strong understanding of:

-AI platform engineering

-LLM pricing mechanics (token billing, context windows)

-GPU infrastructure economics

-Provisioned throughput / reserved capacity

-Cloud commitment strategies

-Kubernetes-based ML workloads

-Cloud billing exports and APIs

-Experience building forecasting and financial models for variable usage systems.

-Experience embedding FinOps practices within engineering teams.

-Strong analytical skills (SQL, Python, Excel/Sheets, BI tools).

-Ability to interpret GPU utilization, inference latency, and throughput metrics.

-Understanding of inference optimization techniques.

-Ability to communicate complex cost structures to technical and non-technical stakeholders.