Senior AI/ML Engineer - Remote, Cloud + MLOps

UnitedHealth Group; Tempe (AZ); Remote; USD 120,000 - 215,000; Full time

Job summary

UnitedHealth Group is seeking a Senior AI/ML Engineer to design, deploy, and support enterprise AI/ML solutions that improve clinical outcomes, patient engagement, and operational efficiency. You will build production-grade ML systems and implement scalable MLOps while partnering with cross-functional teams.

The role requires hands-on experience across ML, cloud platforms, software engineering, and data systems to operationalize AI capabilities and drive measurable impact.

Qualifications

• 5+ years of software development and/or AI/ML engineering experience.
• 5+ years of Python experience.
• 5+ years of experience deploying cloud-based applications and models in Azure, AWS, or Google Cloud Platform.
• 5+ years of experience implementing CI/CD pipelines and software engineering best practices.

Responsibilities

• Design, develop, and deploy AI-powered solutions and ML models for enterprise needs.
• Build, optimize, and maintain scalable AI/ML services and data pipelines.
• Leverage enterprise AI tools to streamline workflows and automate tasks.
• Implement CI/CD and MLOps for reliable production deployments and model governance.
• Create data pipelines using structured and unstructured healthcare data to improve model accuracy.
• Monitor ML systems in cloud environments for stability and security.
• Evaluate emerging AI technologies to inform design and capabilities.
• Collaborate with PMs, architects, data scientists and engineers on scalable designs.
• Mentor junior engineers and contribute to engineering best practices.

Skills

Python

Cloud platforms

CI/CD

SQL / PySpark

Distributed computing

Tools

Azure

AWS

GCP

Databricks

Job description

UnitedHealth Group is seeking a Senior AI/ML Engineer to design, deploy, and support enterprise AI/ML solutions that improve clinical outcomes, patient engagement, and operational efficiency. You will build production-grade ML systems and implement scalable MLOps while partnering with cross-functional teams.

The role requires hands-on experience across ML, cloud platforms, software engineering, and data systems to operationalize AI capabilities and drive measurable impact.