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
- Robust 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)
- Datadog Cloud Cost Management, cloudability or equivalent
- Cost attribution, anomaly detection, and unit economics modeling
- 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