AI - ML DevOps Engineer

HonorHealth

. Design and implement environments that deploy scalable AI/ML solutions, including predictive models, large language model use cases, and agentic workflows .

Posted 11/7/2026full-timeArizona • United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and implementing scalable AI/ML solutions, with a strong focus on DevOps practices, cloud deployment, and compliance with data governance and security standards. Proficient in managing end-to-end machine learning pipelines and collaborating effectively with cross-functional teams.

Highest-signal resume keywords

AI/ML Solution DesignDevOps PracticesCloud Deployment (GCP)MLOpsSQL and Data Engineering

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningPredictive ModelsCI/CDContainerizationInfrastructure as CodeAutomated TestingAPI DevelopmentData ProcessingFeature EngineeringModel Evaluation

Soft Skills

Analytical SkillsProblem-SolvingCommunicationCollaboration

Tools & Technologies

Google Cloud PlatformGitMCP ServersMonitoring ToolsAlerting Systems

Industry Keywords

Data GovernancePrivacySecurityResponsible AIHIPAA Compliance

Tech Stack

Tools & technologies

CloudGoogle Cloud PlatformSQL

About the role

Key responsibilities & impact

Design and implement environments that deploy 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 ingestion, 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
Implement observability and performance evaluation practices for resource utilization, performance, and cost efficiency
Ensure AI/ML and agentic solutions comply with data governance, privacy, security, responsible AI, and HIPAA-aligned expectations
Create and maintain technical documentation for deployments, workflows, operational procedures, assumptions, limitations, and support processes
Troubleshoot pipeline, infrastructure, model, and integration issues and drive continuous improvement
Partner with data engineers, data scientists, AI model developers, platform teams, and business stakeholders

Requirements

What you’ll need

Bachelor's degree in Computer Science, Data Science, Engineering, Artificial Intelligence, Machine Learning, Mathematics, Statistics, or a related quantitative field; or 4 years' relevant experience
3 years of progressive experience in DevOps within a cloud deployment in AI/ML Ops, data engineering, or related software engineering roles
At least 1 year deploying and operating AI/ML production workloads
Hands-on experience with cloud-native AI/ML services and infrastructure, preferably Google Cloud Platform (GCP)
Proficiency in MLOps, DevOps, and software engineering practices, including 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 large-scale data processing, backend engineering, and enterprise data platform integration
Experience with API development and systems integration
Strong analytical and problem-solving skills across data, models, infrastructure, and orchestration layers
Excellent communication and collaboration skills
Must be located in Arizona