Senior AI ML Engineer

HonorHealth•Virtual - Arizona, AZ

2d•Remote

About The Position

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.

Requirements

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
Bachelors or 4 years' experience Required
7 years, of progressive experience in AI/ML engineering, machine learning operations, data engineering, or related software engineering roles Required

Responsibilities

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.
Performs other duties as assigned.