AI Platform FinOps Sr. Engineer

Job Purpose and Impact

• The AI Platform FinOps Sr. Engineer enables cost visibility, financial accountability, and optimization of AI/ML workloads across Cargill's hybrid technology landscape.
  
  This role combines AI platform knowledge, data engineering, and FinOps practices to establish token economics, unit cost models, and cost guardrails, enabling informed trade ‑ offs between 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- Establish and operationalize cost models (token, model, agent level) and enable enterprise‑level AI cost transparency
• Cost Optimization & Guardrails- Identify optimization levers (model selection, token efficiency, workload sizing) and define cost guardrails for AI workloads
• Platform & Workflow Integration - Embed cost signals into CI/CD pipelines, ServiceNow workflows, and AI platform tooling to enable shift‑left decisioning
• Cost Data Engineering & Insights - Develop cost pipelines, attribution models, and dashboards to deliver decision‑ready insights 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 & Showback Models: Design and operate chargeback systems that fairly allocate AI infrastructure costs to consuming brand teams, enabling transparent cost-benefit analysis of AI adoption.

Scope & Complexity

• Works independently on complex, cross-platform AI cost and economics problems
• Influences decisions across AI, cloud, and data platform teams
• Owns end‑to‑end problem areas, including design, implementation, and adoption
• Drives FinOps capability creation in an emerging domain (AI FinOps)

Qualifications

• Minimum requirement of 10 years of relevant work experience. Min. 5 years in engineering-led FinOps / Technology Economics role

• Bachelor's or Master's degree in Engineering, Computer Science, or related field

• Experience in:

• Cloud platforms (Azure, AWS)

• AI/ML services (Azure OpenAI, Bedrock and 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:

• TrueFoundry, AgentCore, LangSmith, Abacus.ai, Pinecone

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

• Experience with:

• Datadog Cloud Cost Management, cloudability or equivalent

• 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)