Job Purpose and Impact :

The AI Platform FinOps Sr. Engineer enables 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 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.

- Minimum 5 years in engineering-led FinOps / Technology Economics role.

- Bachelors or Masters 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).