AI FinOps Engineer

T. Rowe Price • Owings Mills, MD

2d • $122,000 - $261,000 • Hybrid

About The Position

We are seeking an AI FinOps Engineer to help drive the cost-effective, scalable, and well-governed adoption of artificial intelligence across the firm. This role will sit at the intersection of AI platform engineering, cloud financial operations, data infrastructure, and enterprise governance, helping ensure that AI capabilities are delivered with strong financial discipline, operational transparency, and risk awareness.
The AI FinOps Engineer will work closely with teams across TRP Labs, Enterprise Architecture, Engineering, Finance, Procurement, Data Science, Security, Risk, and business stakeholders to provide visibility into AI-related spend, improve resource efficiency, support forecasting and budgeting, and help establish standards for sustainable AI usage.
This role will support a broad range of AI workloads, including machine learning, advanced analytics, and generative AI use cases, across cloud and enterprise technology environments. This is a technical, hands-on role. You will work at the API level to instrument workloads, identify inefficiencies, and engineer solutions that reduce organizational cost without degrading capability. A key output of this work is translating AI usage findings into best practices.

Requirements

• Bachelor’s degree or equivalent work experience in Computer Science, Engineering, Information Systems, Finance, Data Analytics, or a related field.
• Experience in one or more of the following areas: FinOps, cloud engineering, platform engineering, DevOps, MLOps, data engineering, or infrastructure cost management.
• Deep familiarity with LLM pricing mechanics: context windows, caching, batching, input/output token splits, and tier structures.
• Strong understanding of cloud cost drivers, including compute, storage, networking, and managed platform services.
• Familiarity with AI/ML workload patterns such as model training, fine-tuning, batch inference, real-time inference, and data pipeline processing.
• Experience with at least one major cloud platform, such as AWS or Azure.
• Experience using cloud cost management, observability, or reporting tools.
• Proficiency in Python, SQL, or similar scripting/query languages.
• Experience building dashboards, reports, or analytics to support cost transparency and operational decision-making.
• Strong analytical and problem-solving skills with the ability to translate technical usage into financial and business insights.
• Strong communication and collaboration skills, with the ability to work effectively across technical, operational, and business teams.

Nice To Haves

• Experience supporting generative AI or large language model workloads, including token-based pricing models and inference cost optimization.
• Familiarity with enterprise AI and data platforms such as Databricks, Snowflake, SageMaker, Azure ML, or similar technologies.
• Understanding of GPU utilization, accelerator economics, and performance-cost tradeoff analysis.
• Knowledge of enterprise tagging, cost allocation, and budget governance practices.
• Experience in a financial services or other highly regulated environment.
• Familiarity with enterprise risk, security, and control expectations related to technology and data platforms.
• Relevant certifications in cloud platforms, FinOps, Technology Business Management or infrastructure engineering.

Responsibilities

• Develop and maintain cost transparency for AI and machine learning workloads, including compute, storage, networking, model training, inference, and third-party AI platform usage.
• Create and manage reporting, dashboards, and KPIs to track AI-related spend, utilization, efficiency, and business value across teams and use cases.
• Partner with architecture, engineering, platform, and data science teams to identify opportunities to improve cost, performance, and utilization of AI infrastructure and services.
• Analyze AI workload consumption patterns and recommend optimization strategies related to: (1) model selection and deployment approach (2) compute and GPU sizing (3) workload scheduling (4)storage lifecycle management (5) inference efficiency (6) vendor and API usage
• Support the design and implementation of showback and chargeback models for AI-related services to improve accountability and decision-making.
• Build forecasting and budgeting models for AI platform usage, cloud consumption, and external vendor spend.
• Help define and enforce lightweight governance standards for AI infrastructure, including tagging, budgeting, provisioning controls, usage monitoring, and lifecycle management.
• Collaborate with Finance and Procurement to support vendor evaluation, pricing analysis, contract planning, and consumption optimization for AI platforms and services.
• Partner with Risk, Security, and Compliance stakeholders to ensure AI cost optimization practices align with enterprise controls and regulatory expectations.
• Automate cost management and governance processes using scripting, infrastructure-as-code, and cloud-native tooling.
• Evaluate tradeoffs among hosted AI services, internally managed platforms, and open-source model deployments with a focus on cost, scalability, security, and operational supportability.
• Contribute to firmwide best practices for responsible, efficient, and scalable AI adoption.

Benefits

• Competitive compensation
• Annual bonus eligibility
• A generous retirement plan
• Hybrid work schedule
• Health and wellness benefits, including online therapy
• Paid time off for vacation, illness, medical appointments, and volunteering days
• Family care resources, including fertility and adoption benefits

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: Associate degree