Remote MLOps Intern - CI/CD & Model Deployment

Intermountain Healthcare Inc.; Harrisburg (Dauphin County); Remote; USD 30,000 - 44,000; Full time

Benefits offered by this job

Remote internship

Job summary

Intermountain Health is offering a fully remote Machine Learning Operations Intern position. You will join the Enterprise AI Center of Enablement to deploy and maintain infrastructure for AI products, working Monday–Friday during regular business hours in a remote internship.

The role emphasizes developing and maintaining ML pipelines, implementing IaC, and collaborating with data scientists and MLOps engineers while ensuring production readiness and cost efficiency.

Qualifications

• Understanding of statistical and machine learning modeling techniques.
• Experience programming using Python, SQL, Spark, and Matlab.
• Strong communication skills, both verbal and written.

Responsibilities

• Build and maintain CI/CD pipelines for ML models and AI apps.
• Deploy and monitor ML models on UEAA.
• Package models and dependencies for reproducible deployment.
• Develop and maintain Infrastructure as Code to provision resources.
• Support model deployment and lifecycle management within UEAA.
• Implement monitoring for model performance and data drift.
• Optimize SQL queries, Python code and data pipelines for performance and cost.
• Contribute to automated testing and validation of models and pipelines.
• Collaborate with data scientists and MLOps engineers and document processes.

Skills

Statistical modeling

Machine learning techniques

Python programming

Effective communication

Team collaboration

Tools

Git

DevOps

CI/CD

Terraform

Job description

Intermountain Health is offering a fully remote Machine Learning Operations Intern position. You will join the Enterprise AI Center of Enablement to deploy and maintain infrastructure for AI products, working Monday–Friday during regular business hours in a remote internship.

The role emphasizes developing and maintaining ML pipelines, implementing IaC, and collaborating with data scientists and MLOps engineers while ensuring production readiness and cost efficiency.