AI FinOps Engineer

T. Rowe Price

Confirmed live 2 days ago High trust
Hybrid

Quick summary

Work type
Hybrid
Location
Owings Mills, MDBaltimore, MD
Salary
$122,000–$209,000 / yr
Posted
14 days ago
Freshness
Confirmed live 2 days ago

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $178k
This role $166k
$110k most similar roles pay here $237k

This role pays less than 55% of similar roles. Most pay $142,000–$214,931 — the shaded band above. At the midpoint, this role pays about $166k versus about $178k for comparable roles.

Based on 240 similar postings.

Employer

About T. Rowe Price

T. Rowe Price is an asset management firm focused on delivering global investment management excellence and retirement services

T. Rowe Price currently has 26 open roles on FindRole.

Listed pay typically runs $121,500–$207,500 across 26 roles with salary data.

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View all roles at T. Rowe Price

At a glance

TL;DR · AI FinOps Engineer

The AI FinOps Engineer joins a team focused on the cost-effective, scalable, and well-governed adoption of artificial intelligence across the firm. This technical, hands-on role sits at the intersection of AI platform engineering, cloud financial operations, data infrastructure, and enterprise governance. You will work at the API level to instrument workloads, identify inefficiencies, and engineer solutions that reduce costs for machine learning, advanced analytics, and generative AI use cases. Key responsibilities include developing cost transparency for compute, storage, and networking, creating dashboards to track spend, and building forecasting models for cloud consumption. Required skills include proficiency in Python and SQL, experience with AWS or Azure, and a deep understanding of LLM pricing mechanics like token splits and context windows. You will also manage infrastructure tagging, budget governance, and performance-cost tradeoff analysis across various AI platforms.

What you'll do

  • Develop and maintain cost transparency for AI and machine learning workloads including compute, storage, and third-party platform usage.
  • Create dashboards and KPIs to track AI-related spend, utilization efficiency, and business value across various teams.
  • Analyze AI workload consumption patterns to recommend optimization strategies for model selection, GPU sizing, and inference efficiency.
  • Design and implement showback and chargeback models to improve accountability for AI-related services.
  • Build forecasting and budgeting models for cloud consumption and external vendor spend related to AI platforms.
  • Define and enforce governance standards including tagging, provisioning controls, and usage monitoring for AI infrastructure.
  • Automate cost management and governance processes using scripting, infrastructure-as-code, and cloud-native tools.
  • Evaluate trade-offs between hosted services, internal platforms, and open-source models based on cost and scalability.

What we're looking for

  • Bachelor’s degree in Computer Science, Engineering, Information Systems, Finance, Data Analytics, or a related field.
  • Experience in FinOps, cloud engineering, platform engineering, DevOps, MLOps, data engineering, or infrastructure cost management.
  • Deep familiarity with LLM pricing mechanics including context windows, caching, batching, and token structures.
  • Strong understanding of cloud cost drivers such as compute, storage, networking, and managed platform services.
  • Familiarity with AI/ML workload patterns like model training, fine-tuning, and inference processing.
  • Experience with at least one major cloud platform such as AWS or Azure.
  • Proficiency in Python, SQL, or similar scripting and query languages.
  • Relevant certifications in cloud platforms, FinOps, Technology Business Management, or infrastructure engineering.

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