Senior AI/ML DevOps Engineer

Role overview A senior engineering role operationalizing machine learning and AI across a large healthcare system, building the pipelines, infrastructure, and governance that move models from prototype to production. The position partners closely with data engineers, data scientists, platform teams, and business stakeholders to deliver reliable AI capabilities aligned to clinical and operational standards.

Responsibilities - Lead design and implementation of scalable environments for predictive models, large language model use cases, and agentic workflows - Develop and maintain end-to-end ML and retrieval-augmented generation pipelines, including ingestion, feature engineering, training, evaluation, deployment, and lifecycle management - Architect production-grade DevOps practices including CI/CD, model versioning, automated testing, monitoring, alerting, retraining, and rollback - Deploy and manage AI/ML applications in cloud environments with attention to security, reliability, performance, and cost efficiency - Establish observability and performance evaluation to track compute utilization, latency, and operational health - Ensure solutions meet data governance, privacy, security, and responsible AI expectations, including HIPAA-aligned practices where applicable - Troubleshoot complex pipeline, infrastructure, model, and integration issues and drive continuous improvement in stability and delivery efficiency

Requirements - Bachelor's degree in Computer Science, Data Science, Engineering, AI/ML, Mathematics, Statistics, or a related quantitative field, or four years of relevant experience - Seven years of progressive experience in DevOps within cloud deployments, AI/ML Ops, data engineering, or related software engineering roles, including at least three years deploying and operating AI/ML production workloads - Hands-on experience with cloud-native AI/ML services and infrastructure, preferably on Google Cloud Platform - Strong proficiency in MLOps, DevOps, and software engineering practices including CI/CD, Git workflows, containerization, Infrastructure as Code, and automated testing - Strong SQL, data engineering, and API integration skills across enterprise data platforms - Excellent communication skills with the ability to explain technical concepts to non-technical stakeholders

Benefits and work setup - Remote within Arizona, Monday through Friday daytime hours, with occasional on-site work as needed