Senior AI ML Engineer

About the role

At HonorHealth, you’ll find a culture built on warmth and neighborly kindness, supporting the health and well-being of people and communities across the greater Phoenix area. This remote role, based in Arizona, involves occasional on-site work as needed.

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.

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.

Qualifications

• Bachelor’s degree or 4 years of equivalent experience.
• 7 years of progressive experience in AI/ML engineering, machine learning operations, data engineering, or related software engineering roles.

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