AI - ML DevOps Engineer

Location: Remote within Arizona, United States; occasional onsite work as needed. Hybrid workplace, Monday–Friday daytime schedule.

Company

hirify.global is a nonprofit healthcare system serving communities across the greater Phoenix area.

What you will do

• Design and implement environments for scalable AI/ML solutions, including predictive models, LLM use cases, and agentic workflows.
• Develop and maintain end-to-end machine learning and RAG pipelines covering ingestion, feature engineering, training, evaluation, deployment, and lifecycle management.
• Establish production DevOps practices including CI/CD, model versioning, automated testing, monitoring, alerting, retraining, and rollback strategies.
• Deploy and manage secure, reliable, performant AI/ML applications in cloud environments, preferably Google Cloud Platform.
• Implement observability and performance evaluation for resource utilization, system performance, and cost efficiency.
• Ensure governance, privacy, security, responsible AI, and HIPAA-aligned practices; document, troubleshoot, and continuously improve operational processes.

Requirements

• Bachelor’s degree in Computer Science, Data Science, Engineering, Artificial Intelligence, Machine Learning, Mathematics, Statistics, or a related quantitative field, or 4 years of relevant experience.
• At least 3 years of progressive experience in DevOps within cloud deployment, AI/ML operations, data engineering, or related software engineering roles, including at least 1 year deploying and operating AI/ML production workloads.
• Hands-on experience with cloud-native AI/ML services and infrastructure, preferably GCP, including training, inference, orchestration, and scalable compute.
• Proficiency in MLOps, DevOps, CI/CD, Git-based workflows, containerization, Infrastructure as Code, and automated testing.
• Experience establishing connections through API calls and MCP servers, developing APIs, and integrating systems into enterprise applications and workflows.
• Strong SQL and data engineering skills, including large-scale data processing, backend engineering, and integration across enterprise data platforms.
• Strong analytical, troubleshooting, communication, documentation, and collaboration skills across technical and non-technical stakeholder groups.

Culture & Benefits

• Hybrid work arrangement with remote work from Arizona and occasional onsite attendance.
• Full-time, day-shift schedule from Monday through Friday.
• Work supporting clinical, operational, and business objectives within a healthcare environment.