Cloud FinOps AI Engineer

We are seeking a highly technical Senior Cloud FinOps Engineer specialized in designing, developing, and deploying AI-powered agents and automation systems that proactively monitor, analyze, and optimize multi-cloud spend (AWS, Azure, GCP) in a large-scale research and academic healthcare environment.

Responsibilities :

• Research, design, and develop AI / ML-driven agents and automation workflows that continuously ingest cloud billing, usage, and tagging data (via APIs such as AWS Cost Explorer, Azure Cost Management + Billing, GCP Billing exports, CUR, etc.).
• Build predictive models to forecast spend, identify upcoming eligibility for Savings Plans / Reservations, and recommend optimal purchase strategies (term length, payment option, instance family / region / zone / SKU, convertible vs standard) while factoring in performance SLAs and workload variability typical of research computing.
• Implement real-time anomaly and spike detection with intelligent alerting (Slack, email, ServiceNow, etc.) that includes root-cause analysis and suggested corrective actions.
• Develop automated tagging governance engines that detect missing / incorrect tags, suggest or auto-apply corrections (via Lambda / Functions / Azure Automation), and enforce research grant and department chargeback policies.
• Create “recommendation-as-code” pipelines that generate executable Infrastructure-as-Code (Terraform / CloudFormation / Bicep) or direct API calls to purchase / commit to the optimal savings instruments.
• Design and maintain a centralized FinOps AI dashboard (Power BI + custom web frontend if needed) that surfaces agent-generated insights, confidence scores, projected savings, and one-click approval workflows.
• Integrate the AI platform with existing tooling (AWS Cost Anomaly Detection, Azure Advisor, third-party FinOps platforms) and extend them where native capabilities fall short.
• Collaborate on containerized / microservice architecture (Kubernetes / EKS / AKS / GKE) for the agent platform and ensure all components meet healthcare security and compliance standards.
• Continuously measure savings attribution, model accuracy, and automation adoption; iterate on models using retraining pipelines and feedback loops.
• Document architectures, create runbooks, and mentor FinOps analysts and cloud engineers on using the new AI capabilities.

Requirement :

$10M annual cloud spend preferred).

Nice to have :