Senior AI Financial Operations Analyst

Vizient•Chicago, IL

1d•$102,400 - $179,000

About The Position

The Senior AI Financial Operations Analyst will lead financial governance, operational visibility, and consumption optimization for Vizient’s enterprise AI platforms and generative AI services. The role helps establish AI FinOps capabilities that support responsible, scalable, and cost-effective adoption of AI across the organization. Reporting to technology leadership, the analyst will drive AI financial management across cloud AI services, agentic platforms, token-based consumption models, GPU infrastructure, and AI developer tooling. The position combines financial and technical understanding with strategic partnership to translate AI consumption patterns into actionable insights that optimize costs, improve operational efficiency, and maximize business value.

Requirements

At least 5 years of experience in Cloud FinOps, AI Infrastructure, Cloud Operations, Data Platforms, Technology Finance, or a related discipline.
Experience with public cloud AI services, generative AI platforms, and cloud-based consumption models.
Strong understanding of AI pricing and consumption structures, including token-based, inference-based, and GPU-based cost models.
Experience analyzing cloud financial data, operational telemetry, utilization trends, and technology spending patterns.
Demonstrated ability to build forecasting models, perform financial analysis, and develop executive-level reporting and dashboards.
Strong analytical and problem-solving skills, including the ability to identify optimization opportunities and quantify business impact.
Experience collaborating with engineering, architecture, infrastructure, and business stakeholders to drive technology optimization initiatives.
Excellent communication and presentation skills, including the ability to translate complex technical and financial concepts for executive audiences.
Strong knowledge of cloud economics, technology financial management, and AI operational best practices.
Ability to influence cross-functional teams and drive alignment across diverse stakeholder groups while balancing innovation, governance, and financial accountability.

Nice To Haves

Bachelor’s degree in Finance, Accounting, Information Technology, Computer Science, Data Analytics, Business, or a related field preferred.
Experience with Azure OpenAI, OpenAI Enterprise, AWS Bedrock, Google Vertex AI, Azure AI Foundry, or similar AI platforms.
Experience using Cloudability, Azure Cost Management, Datadog, Power BI, or comparable analytics and financial management tools.
Familiarity with AI agent architectures, orchestration frameworks, and emerging AI operational technologies.
FinOps Foundation certification, cloud platform certifications, or related professional credentials.
Experience managing GPU infrastructure or optimizing AI workloads for performance and cost efficiency.
Understanding of AI governance frameworks, responsible AI practices, and enterprise risk management considerations.

Responsibilities

Lead AI financial operations across enterprise AI platforms and monitor and manage cloud-based AI spending.
Track, analyze, and optimize AI consumption metrics, including token utilization, inference costs, GPU usage, model hosting expenses, vector database consumption, embeddings, and AI agent execution patterns.
Develop forecasting models and financial planning processes to support platform growth, capacity management, and long-term investment strategies.
Establish and maintain AI showback and chargeback methodologies that provide transparency into consumption and business value realization.
Define and implement governance standards, financial guardrails, and operational controls for responsible and cost-effective AI adoption.
Partner with Security, Risk, Legal, Architecture, Cloud Infrastructure, Data & Analytics, and AI Engineering teams to align governance with organizational policies and strategic objectives.
Provide visibility into AI tooling adoption and identify and mitigate unsanctioned or shadow AI usage.
Collaborate with engineering teams to optimize AI workloads through prompt efficiency, token utilization, model selection, inference routing, caching, batching, and GPU resource allocation improvements.
Evaluate hosted and self-managed AI infrastructure options and provide cost optimization recommendations and economic analyses for AI architectures and agent-based frameworks.
Develop executive dashboards, reporting, and analytics on AI consumption trends, financial performance, operational efficiency, and return on investment.
Establish AI unit economics and performance metrics, tracking costs and value realization across use cases, workflows, business capabilities, and enterprise initiatives.
Present financial insights, optimization opportunities, and strategic recommendations to executive leadership and stakeholders.
Support enterprise AI investment planning, governance programs, and development of scalable AI operating models and financial management capabilities.
Use data-driven insights to continuously improve AI financial governance, reporting processes, and operational effectiveness.