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Lead AI FinOps capability: build token and unit cost models, embed cost signals into platforms and CI/CD, create cost pipelines, governance, forecasts, dashboards, and chargeback systems to enable cost-aware AI decisions and optimization.
Job Purpose and Impact
Key Accountabilities
Scope & Complexity
Qualifications
Preferred Qualifications
- 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)
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