AI FinOps Engineer - Cost-Driven ML & Cloud Platform

troweprice; United States; On-site; USD 120,000 - 210,000; Full time

Job summary

T. Rowe Price is seeking an AI FinOps Engineer to drive cost‑effective, scalable AI adoption across the firm. You will work at the intersection of AI platform engineering, cloud financial operations, data infrastructure, and governance to deliver financial discipline and transparency.

This hands‑on role involves instrumenting workloads, identifying inefficiencies, and engineering cost‑saving solutions while maintaining performance for AI workloads across cloud and on‑prem environments.

Qualifications

• Bachelor's degree or equivalent work experience in a related field.
• Experience in FinOps, cloud engineering, platform engineering, DevOps, MLOps, data engineering, or infrastructure cost management.
• Deep familiarity with LLM pricing models and AI platform economics.

Responsibilities

• Develop and maintain cost transparency for AI and ML workloads across compute, storage, networking, model training, inference, and third‑party usage.
• Create reports, dashboards, and KPIs to track AI spend, utilization, efficiency, and business value.
• Collaborate with architecture, engineering, data science, finance, and security teams to optimize AI infrastructure costs and performance.
• Analyze AI workload patterns and recommend optimization for deployment, compute sizing, scheduling, and storage lifecycle.
• Support design of showback/chargeback models to improve accountability and decision making.
• Build forecasting models for AI platform usage, cloud consumption, and external vendor spend.

Skills

FinOps

Cloud engineering

Platform engineering

DevOps

MLOps

Data engineering

Infrastructure cost management

Education

Bachelor's degree or equivalent in Computer Science, Engineering, Information Systems, Finance, Data Analytics, or related field

Job description

T. Rowe Price is seeking an AI FinOps Engineer to drive cost‑effective, scalable AI adoption across the firm. You will work at the intersection of AI platform engineering, cloud financial operations, data infrastructure, and governance to deliver financial discipline and transparency.

This hands‑on role involves instrumenting workloads, identifying inefficiencies, and engineering cost‑saving solutions while maintaining performance for AI workloads across cloud and on‑prem environments.