Senior MLOps Engineer: Deploy & Scale ML Models

Aimlroles; Atlanta (GA); On-site; USD 143,000 - 169,000; Full time

Benefits offered by this job

Innovation & Technology

Exposure to World Class Leaders

Agile Work Environment

Job summary

The Coca-Cola Company’s Technology organization is undergoing a digital transformation to empower employees with world-class technology for connecting products to customers globally. This role focuses on turning data into actionable insights, developing analytics models, and guiding junior MLOps engineers in a collaborative, innovative environment.

You will deploy and operationalize ML models, automate pipelines, and monitor model performance in production while working with Azure ML, AKS, and

Qualifications

• 6+ years of professional experience in MLOps, Data Engineering, Software Engineering, or a related field.
• 3+ years of experience managing and scaling high-performing MLOps or data platform teams, with a focus on mentorship.
• Cloud ML Platforms: hands-on with at least one major cloud ML platform (Azure ML preferred; AWS SageMaker / GCP Vertex AI acceptable).

Responsibilities

• Model Deployment & Operationalization: containerize models for scalable deployments.
• Pipeline Automation: build and maintain CI/CD pipelines for ML workflows using GitHub Actions.
• Utilize Azure ML and Microsoft Fabric Data Science to manage the ML lifecycle.
• Monitoring & Maintenance: track model performance and data drift in production.

Skills

Python

Git

CI/CD

Team leadership

MLOps

Tools

GitHub Actions

Docker

Kubernetes

Azure ML

Azure Container Registry

Job description

The Coca-Cola Company’s Technology organization is undergoing a digital transformation to empower employees with world-class technology for connecting products to customers globally. This role focuses on turning data into actionable insights, developing analytics models, and guiding junior MLOps engineers in a collaborative, innovative environment.

You will deploy and operationalize ML models, automate pipelines, and monitor model performance in production while working with Azure ML, AKS, and