The Role
Designs, develops, deploys, and supports scalable AI/ML solutions, including predictive models, large language model applications, and agentic workflows. Builds end-to-end machine learning pipelines and production MLOps practices, including deployment, monitoring, testing, retraining, and rollback. Ensures solutions meet security, privacy, governance, HIPAA-aligned, and responsible AI expectations. Troubleshoots complex data, infrastructure, and model issues while documenting systems and contributing to AI engineering standards and roadmaps.
Summary Generated by Built In
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 Senior AI / ML 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.
ESSENTIAL FUNCTIONS
- Leads the design and implementation of 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 pipelines spanning data ingestion, feature engineering, training, evaluation, deployment, and lifecycle management.
- Architects and supports production-grade MLOps practices, including CI/CD, model versioning, automated testing, monitoring, alerting, retraining, and rollback strategies.
- Deploys and manages AI/ML solutions in cloud environments, ensuring solutions are secure, reliable, performant, and operationally supportable.
- Implements observability and performance evaluation practices to detect drift, degradation, failures, and behavioral anomalies in models and intelligent agents.
- 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 architectures, models, 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.
- Evaluates emerging AI/ML tools, platforms, and practices and contributes to technical standards, reusable patterns, and the long-term roadmap for AI engineering capabilities.
- 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
- Knowledge of MLOps, DevOps, and software engineering practices such as CI/CD, Git-based workflows, containerization, Infrastructure as Code, and automated testing
- Strong SQL and data engineering skills with experience in large-scale data processing, feature 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 or 4 years' experience Required
EXPERIENCE
- 7 years, of progressive experience in AI/ML engineering, machine learning operations, data engineering, or related software engineering roles Required
LICENSE AND CERTIFICATIONS
We're all in for your career.
Skills Required
- Bachelor's degree or four years of relevant experience
- Seven years of progressive experience in AI/ML engineering, machine learning operations, data engineering, or related software engineering
- Hands-on experience with cloud-native AI/ML services and infrastructure
- Experience with Google Cloud Platform services for training, inference, orchestration, and scalable compute
- Knowledge of MLOps, DevOps, CI/CD, Git-based workflows, containerization, Infrastructure as Code, and automated testing
- Strong SQL and data engineering skills, including large-scale data processing, feature engineering, and enterprise data integration
- Experience developing APIs and integrating systems to embed AI/ML capabilities into enterprise applications and workflows
- Strong analytical and problem-solving skills across data, models, infrastructure, and orchestration layers
- Excellent communication, collaboration, technical documentation, and stakeholder-explanation skills
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The Company
What We Do
This practice is a dedicated team of electrophysiologists, physician assistants, nurses and technicians, who as a group, are committed to treating and caring for patients experiencing problems with their heart's rhythm and electrical signaling.
