Designs, deploys, and operates scalable AI/ML solutions, including predictive models, large language model applications, agentic workflows, and RAG pipelines. Builds CI/CD, model versioning, testing, monitoring, retraining, and rollback capabilities. Manages cloud deployments, observability, security, governance, HIPAA-aligned practices, and system integrations. Troubleshoots data, infrastructure, model, and orchestration issues while collaborating with data engineers, data scientists, platform teams, and business stakeholders.
Primary City/State:
Virtual ArizonaCategory:
Data IntelligenceShift:
DayDepartment:
Augmented IntelligenceHours: Monday-Friday Days
Location: Remote -- Must be located in Arizona -- Occasional on site as needed.Great care starts with great people. (Like you.)
At HonorHealth, you’ll find something special. From humble beginnings in 1927 to one of Arizona’s largest nonprofit healthcare systems, our culture is built on warmth and neighborly kindness. Behind every smile is a highly skilled professional with deep expertise and an unwavering dedication to what matters most — caring for the health and well-being of people and communities across the greater Phoenix area.
Responsibilities:
JOB SUMMARY
The AI / ML DevOps Engineer designs, develops, deploys, and supports scalable machine learning solutions that enable advanced analytics and AI capabilities across HonorHealth. This role operationalizes models and pipelines, monitors performance, and partners with data, engineering, and stakeholders to deliver reliable solutions aligned to governance and data handling expectations. The role partners closely with data engineers, data scientists, AI model developers, platform teams, and business stakeholders to translate complex needs into production-ready deployments, monitor ongoing performance, and continuously improve AI/ML capabilities aligned to governance and operational standards.
ESSENTIAL FUNCTIONS
- Design and implementation of the environment that deploys scalable AI/ML solutions, including predictive models, large language model use cases, and agentic workflows that support clinical, operational, and business objectives.
- Develops and maintains end-to-end machine learning and RAG pipelines supporting data ingestion, application feature engineering, training, evaluation, deployment, and lifecycle management.
- Architects and supports production-grade DevOps practices, including CI/CD, model versioning, automated testing, monitoring, alerting, retraining, and rollback strategies.
- Deploys and manages AI/ML applications in cloud environments, ensuring solutions are secure, reliable, performant, and operationally supportable.
- Implements observability and performance evaluation practices to manage compute resource utilization, performance, and cost efficiency. Works with AI development team to a continuous improvement feedback loop.
- Ensures AI/ML and agentic solutions comply with data governance, privacy, security, and responsible AI expectations, including HIPAA-aligned practices where applicable.
- Creates and maintains technical documentation for deployments, workflows, operational procedures, assumptions, limitations, and support processes.
- Troubleshoots complex pipeline, infrastructure, model, and integration issues; implements fixes and drives continuous improvement in operational stability and delivery efficiency.
- Core Skills: Hands-on experience with cloud-native AI/ML services and infrastructure, preferably in Google Cloud Platform (GCP), including services for training, inference, orchestration, and scalable compute Proficiency info MLOps, DevOps, and software engineering practices such as CI/CD, Git-based workflows, containerization, Infrastructure as Code, and automated testing Proficiency with establishing connections via API calls and MCP servers. Strong SQL and data engineering skills with experience in 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 with the ability to diagnose issues across data, models, infrastructure, and orchestration layers Excellent communication and collaboration skills with the ability to document and explain technical concepts clearly to both technical and non-technical stakeholders
- Performs other duties as assigned.
EDUCATION
- Bachelors Computer Science, Data Science, Engineering, Artificial Intelligence, Machine Learning, Mathematics, Statistics, or a related quantitative field; or 4 years’ relevant experience. Required
EXPERIENCE
- 3 years, of progressive experience in DevOps within a Cloud deployment in AI/ML Ops, data engineering, or related software engineering roles, including at least 1 year deploying and operating AI/ML production workloads. Required
LICENSE AND CERTIFICATIONS
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