Senior AI/ML DevOps Engineer - Remote AZ

HonorHealth; Arizona; On-site; USD 140,000 - 190,000; Full time

Job summary

HonorHealth is seeking a Senior AI/ML DevOps Engineer to design, deploy, and support scalable AI/ML solutions across the organization. The role focuses on productionizing models, building robust pipelines, and ensuring governance and security in healthcare data environments.

You will partner with data engineers, data scientists, platform teams, and business stakeholders to translate complex needs into reliable, joinable deployments, monitor performance, and drive continuous improvement in AI

Qualifications

• Bachelor's degree in a quantitative field or 4+ years of relevant experience.
• 7+ years in DevOps, AI/ML Ops, data engineering, or related software engineering roles.
• Experience deploying AI/ML production workloads.
• Strong knowledge of cloud-native AI/ML services.

Responsibilities

• Lead the design and deployment of scalable AI/ML solutions in cloud environments.
• Develop end-to-end MLOps pipelines for data ingestion, training, evaluation, deployment, and lifecycle management.
• Implement CI/CD, model versioning, automated testing, monitoring, and rollback strategies.
• Ensure governance, privacy, and security best practices, incl. HIPAA where applicable.
• Collaborate with data engineers, scientists, platform teams, and business stakeholders.

Skills

MLOps

DevOps

CI/CD

Git workflows

Containerization

APIs

SQL

Data engineering

Cloud computing

Python

Education

Bachelor's degree or equivalent in Computer Science/Data Science/Engineering/AI/ML/Math/Stats

Tools

Google Cloud Platform

Job description

HonorHealth is seeking a Senior AI/ML DevOps Engineer to design, deploy, and support scalable AI/ML solutions across the organization. The role focuses on productionizing models, building robust pipelines, and ensuring governance and security in healthcare data environments.

You will partner with data engineers, data scientists, platform teams, and business stakeholders to translate complex needs into reliable, joinable deployments, monitor performance, and drive continuous improvement in AI