What are the responsibilities and job description for the AI & LLM Infrastructure FinOps Analyst position at Bloomberg L.P.?
Description & Requirements
Overview
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.
Key Responsibilities
AI & LLM Cost Governance
-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.
Provisioned Throughput & Capacity Optimization
-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.
Cost Optimization & Performance Trade-offs
-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
Tooling & Automation
-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, Google Cloud Platform 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.
Required Qualifications
-5 years in FinOps, cloud financial management, or technical finance.
-Direct experience managing cloud infrastructure spend (AWS, Azure, Google Cloud Platform).
-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.
-A Degree in Computer Science, Engineering, Mathematics, similar field of study or equivalent work experience
Salary Range = 00 USD Annually Benefits Bonus
The referenced salary range is based on the Company's good faith belief at the time of posting. Actual compensation may vary based on factors such as geographic location, work experience, market conditions, education/training and skill level.
We offer one of the most comprehensive and generous benefits plans available and offer a range of total rewards that may include merit increases, incentive compensation (exempt roles only), paid holidays, paid time off, medical, dental, vision, short and long term disability benefits, 401(k) match, life insurance, and various wellness programs, among others. The Company does not provide benefits directly to contingent workers/contractors and interns.
Discover what makes Bloomberg unique - watch our podcast series for an inside look at our culture, values, and the people behind our success.
Overview
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.
Key Responsibilities
AI & LLM Cost Governance
-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.
Provisioned Throughput & Capacity Optimization
-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.
Cost Optimization & Performance Trade-offs
-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
Tooling & Automation
-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, Google Cloud Platform 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.
Required Qualifications
-5 years in FinOps, cloud financial management, or technical finance.
-Direct experience managing cloud infrastructure spend (AWS, Azure, Google Cloud Platform).
-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.
-A Degree in Computer Science, Engineering, Mathematics, similar field of study or equivalent work experience
Salary Range = 00 USD Annually Benefits Bonus
The referenced salary range is based on the Company's good faith belief at the time of posting. Actual compensation may vary based on factors such as geographic location, work experience, market conditions, education/training and skill level.
We offer one of the most comprehensive and generous benefits plans available and offer a range of total rewards that may include merit increases, incentive compensation (exempt roles only), paid holidays, paid time off, medical, dental, vision, short and long term disability benefits, 401(k) match, life insurance, and various wellness programs, among others. The Company does not provide benefits directly to contingent workers/contractors and interns.
Discover what makes Bloomberg unique - watch our podcast series for an inside look at our culture, values, and the people behind our success.
Salary : $160,000 - $220,000