Remote AI MLOps Engineer: Deploy & Scale ML

BlueCross BlueShield of Tennessee, Inc.; Chattanooga (TN); Remote; USD 110,000 - 160,000; Full time

Benefits offered by this job

Sponsorship available

Remote-first organization

Job summary

BlueCross BlueShield of Tennessee, Inc. seeks an AI MLOps Engineer to support data science teams in building, deploying, and operating machine learning solutions at scale.

This hands-on role focuses on technical execution, continuous learning, and collaboration with data scientists and engineers in a modern production environment. The position emphasizes deploying models, monitoring performance, and automating ML pipelines, with opportunities to work with OpenShift, Kubernetes, Terraform, and

Qualifications

• Bachelor’s degree in computer science or equivalent work experience.
• 4 years of professional work experience in a corporate environment is considered equivalent.
• Proficiency in Python and data processing for ML workflows.
• Experience with cloud platforms and ML lifecycle tools.

Responsibilities

• Model Deployment: deploy ML models into production environments.
• Model Monitoring: monitor performance and detect drift.
• Automation: automate ML pipelines (preprocessing, training, evaluation).
• Collaboration: work with data scientists, software engineers, and IT operations.
• Version Control and Governance: manage model versioning and governance.
• Optimization: improve performance and scalability of ML systems.
• Explore cloud tools to support ML use cases.

Skills

Python

Cloud platforms

CI/CD

ML lifecycle

Data engineering

Backend APIs

Front-end dashboards

Kubernetes/OpenShift

Terraform

Databricks

Education

Bachelor’s degree in CS or equivalent

Tools

AWS

Azure

GCP

MLflow

OpenShift

Kubernetes

Terraform

Databricks

Job description

BlueCross BlueShield of Tennessee, Inc. seeks an AI MLOps Engineer to support data science teams in building, deploying, and operating machine learning solutions at scale.

This hands-on role focuses on technical execution, continuous learning, and collaboration with data scientists and engineers in a modern production environment. The position emphasizes deploying models, monitoring performance, and automating ML pipelines, with opportunities to work with OpenShift, Kubernetes, Terraform, and