AI Platform FinOps Sr. Engineer

Job Purpose and Impact

• The AI Platform cost visibility financial accountability and optimization of AI/ML workloads across Cargills hybrid technology landscape.

  This role combines AI platform knowledge data engineering and FinOps practices toestablishtoken economics unit cost models andcostguardrails enabling informedtradeoffsbetween cost performance and scale as AI adoption accelerates.

  The position plays a critical role in advancing FinOps into a Technology Economics capability across AI cloud and data platforms.

Key Accountabilities

• AI Cost Visibility & Token Economics-Establishand operationalize cost models (token model agent level) and enableenterpriselevelAI cost transparency
• Cost Optimization & Guardrails-Identifyoptimization levers (model selection token efficiency workload sizing) and definecostguardrails for AI workloads
• Platform & Workflow Integration-Embed cost signals into CI/CD pipelines ServiceNow workflows and AI platform tooling to enableshiftleftdecisioning
• Cost Data Engineering & Insights-Develop cost pipelines attribution models and dashboards to deliverdecisionreadyinsights across AI workloads
• Governance & Automation-Implement policy-based controls anomaly detection and automated enforcement for AI cost management
• Forecasting & Budgeting: Build financial forecasting models for AI workload growth token consumption and infrastructure spend. Provide quarterly and annual budget projections to leadership.
• FinOps Enablement-Partner with platform and product teams to drive adoption and embed cost accountability into engineering and product decisions
• Reporting & Analysis: Create executive dashboards financial health reports and cost trend analysis. Present findings to leadership and brand teams to inform strategic decisions.
• Chargeback &ShowbackModels: Design andoperatechargeback systems that fairlyallocateAI infrastructure costs to consuming brand teams enabling transparent cost-benefit analysis of AI adoption.

Scope & Complexity

• Works independently on complex cross-platform AIcostand economics problems
• Influences decisions across AI cloudanddataplatformteams
• Ownsendtoendproblem areas including design implementation and adoption
• DrivesFinOpscapability creation in an emerging domain (AI FinOps)

Qualifications

• Minimumrequirementof10years of relevant work .5 years inengineering-led FinOps / Technology Economics role

• Bachelors orMasters degree in Engineering Computer Science or related field

• Experience in:

• Cloud platforms (Azure AWS)

• AI/ML services (Azure OpenAIBedrockand emerging AI/ML platforms)

• Data engineering / analytics

• Strong understanding of:

• FinOps principles and cloud cost management

• Distributed systems and API-based consumption models

Preferred Qualifications

• Experience with LLM/token-based pricing models (OpenAI Claude Bedrock APIs)

• Exposure to AI ecosystem tools:

• TrueFoundryAgentCoreLangSmith Pinecone

• Enterprise AI assistants (ChatGPT Enterprise M365 Copilot GitHub Copilot)

• Experience with:

• Datadog Cloud Cost Managementcloudabilityorequivalent

• Cost attribution anomaly detection and unit economics modeling

• Familiarity with:

• CI/CD pipelines and shift-left engineering practices

• Policy-as-code and automated guardrails

• Experience in unit economics modeling (cost per transaction agent or product)