AI FinOps Engineer

What you'll do

• Develop and maintain cost transparency for AI and machine learning workloads including compute, storage, and third-party platform usage.
• Create and manage reporting, dashboards, and KPIs to track AI-related spend, utilization, and business value.
• Analyze AI workload consumption patterns to recommend optimization strategies for model selection, GPU sizing, and inference efficiency.
• Build forecasting and budgeting models for AI platform usage, cloud consumption, and external vendor spend.
• Design and implement showback and chargeback models to improve accountability for AI-related services.
• 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 based on cost and scalability.
• Define and enforce governance standards for AI infrastructure, including tagging, provisioning controls, and usage monitoring.

What we're looking for

• 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 areas including FinOps, cloud engineering, platform engineering, DevOps, MLOps, data engineering, or infrastructure cost management.
• Deep familiarity with LLM pricing mechanics including context windows, caching, batching, input/output token splits, and tier structures.
• Strong understanding of cloud cost drivers such as compute, storage, networking, and managed platform services.
• Familiarity with AI/ML workload patterns including 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.
• Experience supporting generative AI or large language model workloads, including token-based pricing models and inference cost optimization (preferred).
• Familiarity with enterprise AI and data platforms such as Databricks, Snowflake, SageMaker, Azure ML, or similar technologies (preferred).
• Understanding of GPU utilization, accelerator economics, and performance-cost tradeoff analysis (preferred).
• Knowledge of enterprise tagging, cost allocation, and budget governance practices (preferred).
• Experience in a financial services or other highly regulated environment (preferred).
• Familiarity with enterprise risk, security, and control expectations related to technology and data platforms (preferred).
• Relevant certifications in cloud platforms, FinOps, Technology Business Management, or infrastructure engineering (preferred).