**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 + Solid 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)

📌 AI Platform FinOps Sr. Engineer (Bengaluru)
🏢 Cargill
📍 Bengaluru