Sr. AI FinOps Engineer (1701)

6+ years in Cloud Engineering, DevOps, Data Analytics, or related roles. At least 2 years applying FinOps principles in cost management, reporting, or governance. FinOps Certified Practitioner or Professional (mandatory). Experience analyzing costs associated with AI/ML services, including GPU-based workloads, Sagemaker, or Vertex AI. Ability to identify the cost implications of training, inference, storage, and data transfer. Practical experience with AWS cost tools (Cost Explorer, Apptio, CloudHealth, Kion, Cloudability). Understanding of cost allocation models, tagging strategies, budgeting workflows, and saving mechanisms. Strong SQL skills for building cost datasets and running detailed analyses. Advanced Excel or Google Sheets abilities (pivot tables, lookups, automation). Experience with data pipelines or visualization tools such as Datadog, Snowflake, or Stitcher. Solid understanding of cloud architecture (AWS preferred). Exposure to GCP or Snowflake is a plus. Ability to communicate cost insights clearly to both technical and non-technical teams. English level B2+ required for daily interaction.

Standardize OpenAI and AI-related cloud spending within enterprise cost governance frameworks. Ensure consistent visibility into R&D budgets across Datadog, Stitcher, and internal reporting tools. Build and maintain automated data pipelines to replace manual reporting, forecasting, and alerting workflows. Produce clean, auditable datasets to support monthly financial reviews and variance analysis. Analyze AI/ML workloads and identify cost drivers, focusing on GPU usage, training cycles, inference costs, and data movement. Develop automated explanations for cost fluctuations to help engineering teams understand consumption patterns. Document usage guidelines for OpenAI and other AI services. Identify patterns of inefficient consumption and recommend corrective actions. Work with Cloud and Observability teams to pilot tooling for team-level budget tracking, proactive alerts, and compliance with spending targets. Align forecasting models, reporting standards, and cost allocation methodologies with FinOps, Engineering, Cloud, and Finance stakeholders.