# AI Platform FinOps Sr. Engineer

## About the Role

This role is focused on improving cost visibility, financial accountability, and optimization for AI/ML workloads across Cargill’s hybrid technology environment. It combines AI platform knowledge, data engineering, and FinOps practices to build token economics, unit cost models, and cost guardrails that help teams make better trade-offs between cost, performance, and scale.

The position also helps expand FinOps into a broader technology economics capability across AI, cloud, and data platforms. It works closely with platform and product teams, as well as leadership and brand teams, to embed cost awareness into engineering and product decisions.

This is an end-to-end role covering design, implementation, and adoption for complex AI cost and economics problems. The work supports forecasting, governance, reporting, and transparent cost allocation for AI usage.

## Key Responsibilities

– Build and operationalize AI cost models at the token, model, and agent level.
– Improve AI workload efficiency by identifying optimization levers such as model selection, token usage, and workload sizing.
– Define and enforce cost guardrails for AI workloads.
– Integrate cost signals into CI/CD pipelines, ServiceNow workflows, and AI platform tooling.
– Develop cost pipelines, attribution models, dashboards, and decision-ready insights.
– Implement policy-based controls, anomaly detection, and automated enforcement for AI cost management.
– Create forecasting models for AI workload growth, token consumption, and infrastructure spend.
– Prepare quarterly and annual budget projections for leadership.
– Partner with platform and product teams to strengthen FinOps adoption and cost accountability.
– Produce executive dashboards, financial health reports, and cost trend analysis.
– Present findings to leadership and brand teams.
– Design and operate chargeback/showback models for allocating AI infrastructure costs to consuming brand teams.

## Required Skills

– 10+ years of relevant work experience.
– At least 5 years in an engineering-led FinOps or Technology Economics role.
– Bachelor’s or Master’s degree in Engineering, Computer Science, or a related field.
– Experience with cloud platforms, including Azure and AWS.
– Experience with AI/ML services, including Azure OpenAI, Bedrock, and emerging AI/ML platforms.
– Experience in data engineering and analytics.
– Strong understanding of FinOps principles and cloud cost management.
– Strong understanding of distributed systems and API-based consumption models.

## Preferred Skills

– Experience with LLM/token-based pricing models, including OpenAI, Claude, and Bedrock APIs.
– Exposure to AI ecosystem tools such as TrueFoundry, AgentCore, LangSmith, Abacus.ai, and Pinecone.
– Experience with enterprise AI assistants such as ChatGPT Enterprise, M365 Copilot, and GitHub Copilot.
– Experience with Datadog Cloud Cost Management, Cloudability, or a similar tool.
– Experience with cost attribution, anomaly detection, and unit economics modeling.
– Familiarity with CI/CD pipelines and shift-left engineering practices.
– Familiarity with policy-as-code and automated guardrails.
– Experience with unit economics modeling at the cost-per-transaction, agent, or product level.

## Cloud Platforms & Technologies

### Cloud Providers
– Azure
– AWS

### AI/ML Services and Platforms
– Azure OpenAI
– Bedrock
– Emerging AI/ML platforms
– TrueFoundry
– AgentCore
– LangSmith
– Abacus.ai
– Pinecone
– ChatGPT Enterprise
– M365 Copilot
– GitHub Copilot

### FinOps / Cost Management Tools
– Datadog Cloud Cost Management
– Cloudability

### Delivery and Workflow Tools
– CI/CD pipelines
– ServiceNow

### Data, Analytics, and Governance
– Data engineering
– Analytics
– Dashboards
– Cost pipelines
– Attribution models
– Distributed systems
– API-based consumption models
– Policy-as-code
– Automated guardrails
– Anomaly detection

## FinOps Responsibilities

– Establish AI cost visibility through token-, model-, and agent-level cost models.
– Build unit economics models for AI consumption and infrastructure usage.
– Define cost guardrails and optimization strategies for AI workloads.
– Forecast AI workload growth, token consumption, and infrastructure spend.
– Provide quarterly and annual budget projections.
– Design chargeback/showback models for cost allocation to brand teams.
– Deliver cost trend analysis, executive reporting, and financial health insights.
– Implement governance controls and automated enforcement for AI cost management.

## Why You Might Be Interested

This role offers the chance to shape how AI costs are measured, managed, and communicated across a complex hybrid environment. It combines engineering, financial modeling, governance, and executive reporting in a single remit. The scope is broad, spanning AI, cloud, and data platforms, with direct influence on engineering and product decisions. It also contributes to building a new technology economics capability in an emerging domain.