AI - ML DevOps Engineer

HonorHealth • Virtual - Arizona, AZ

1d • Remote

About The Position

The AI / ML DevOps Engineer designs, develops, deploys, and supports scalable machine learning solutions that enable advanced analytics and AI capabilities across HonorHealth. The role operationalizes models and pipelines, monitors performance, and works with data, engineering, and business stakeholders to deliver reliable solutions aligned with governance and data-handling expectations.

The engineer designs deployment environments for AI/ML solutions, including predictive models, large language model use cases, and agentic workflows supporting clinical, operational, and business objectives. The role develops and maintains end-to-end machine learning and RAG pipelines, and establishes production-grade DevOps practices for model versioning, testing, monitoring, retraining, and rollback.

The position deploys and manages AI/ML applications in cloud environments, with attention to security, reliability, performance, operational support, resource utilization, and cost efficiency. It also supports compliance with data governance, privacy, security, and responsible AI expectations, including HIPAA-aligned practices where applicable. The engineer documents deployments and operational procedures, resolves complex pipeline and infrastructure issues, and drives continuous improvement. The position is remote within Arizona, with occasional on-site work as needed, and follows a Monday–Friday day schedule.

Requirements

• Bachelor's degree in Computer Science, Data Science, Engineering, Artificial Intelligence, Machine Learning, Mathematics, Statistics, or a related quantitative field, or four years of relevant experience.
• Three years of progressive experience in DevOps within a cloud deployment in AI/ML Ops, data engineering, or related software engineering roles, including at least one year deploying and operating AI/ML production workloads.
• Hands-on experience with cloud-native AI/ML services and infrastructure, preferably Google Cloud Platform (GCP), including services for training, inference, orchestration, and scalable compute.
• Proficiency in MLOps, DevOps, and software engineering practices such as CI/CD, Git-based workflows, containerization, Infrastructure as Code, and automated testing.
• Proficiency establishing connections via API calls and MCP servers.
• Strong SQL and data engineering skills, including experience with large-scale data processing, backend engineering, and integration across enterprise data platforms.
• Experience with API development and systems integration to embed AI/ML capabilities into enterprise applications and workflows.
• Strong analytical and problem-solving skills to diagnose issues across data, models, infrastructure, and orchestration layers.
• Excellent communication and collaboration skills, including the ability to document and explain technical concepts to technical and non-technical stakeholders.
• Must be located in Arizona.

Responsibilities

• Design and implement environments for scalable AI/ML solutions, including predictive models, large language model use cases, and agentic workflows.
• Develop and maintain end-to-end machine learning and RAG pipelines for data ingestion, application feature engineering, training, evaluation, deployment, and lifecycle management.
• Architect and support production-grade DevOps practices, including CI/CD, model versioning, automated testing, monitoring, alerting, retraining, and rollback strategies.
• Deploy and manage AI/ML applications in cloud environments, ensuring they are secure, reliable, performant, and operationally supportable.
• Implement observability and performance evaluation practices to manage compute utilization, performance, and cost efficiency, and work with the AI development team on continuous improvement.
• Ensure AI/ML and agentic solutions comply with data governance, privacy, security, and responsible AI expectations, including HIPAA-aligned practices where applicable.
• Create and maintain technical documentation for deployments, workflows, operational procedures, assumptions, limitations, and support processes.
• Troubleshoot pipeline, infrastructure, model, and integration issues; implement fixes and improve operational stability and delivery efficiency.
• Perform other duties as assigned.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level