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: Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related quantitative field; advanced degree in a healthcare or research-related discipline is a plus. 5+ years of hands-on cloud engineering and architecture experience with at least two major providers (AWS and Azure required; GCP a plus). 3+ years building production-grade data pipelines, ML models, or intelligent automation in a cloud cost-management or FinOps context. Proven track record of implementing Savings Plans, Reserved Instances, and committed-use discount strategies at scale (> $10M annual cloud spend preferred). Strong software development skills in Python (mandatory) and at least one additional language (Go, TypeScript/Node.js, Java, etc.). Hands-on experience with ML frameworks (scikit-learn, TensorFlow, PyTorch, XGBoost/LightGBM) and MLOps tools (MLflow, SageMaker, Azure ML, Vertex AI). Expertise in cloud billing APIs, Cost and Usage Reports (CUR), Cost Explorer, Azure Consumption APIs, and building enriched data lakes (S3 + Athena/Glue, Azure Data Lake + Synapse, BigQuery). Proficiency in Infrastructure as Code (Terraform primary; CloudFormation/Bicep acceptable) and CI/CD pipelines (GitHub Actions, GitLab CI, Azure DevOps). Experience with event-driven architectures (EventBridge, Azure Event Grid, Pub/Sub) and serverless compute for real-time processing. Solid understanding of tagging strategies, cost allocation, showbacks/chargebacks in decentralized research/academic environments.

Nice to have: Previous work in healthcare, academic medical centers, or grant-funded research environments. FinOps Certified Practitioner or Platform Engineer certification. Contributions to open-source FinOps or cloud-cost tools (e.g., Kubecost, Cloud Custodian, Infracost, custom agents). Experience with generative AI/LLMs for explaining recommendations to non-technical stakeholders. Familiarity with Apache Airflow, dbt, Databricks, or similar for orchestration and transformation. Knowledge of HIPAA/HITECH-compliant data handling and encryption standards in analytics workloads.