ML Ops Manager — Lead Scalable ML Infrastructure

Kohl's; Menomonee Falls (WI); On-site; USD 140,000 - 190,000; Full time

Job summary

Kohl's is seeking a Manager, Machine Learning Operations (ML Ops) to lead a team of engineers in designing, deploying, and operating scalable ML solutions across the MLE ecosystem. You will drive the full ML lifecycle, build ETL pipelines, deploy models to customer-facing apps, and enable efficient delivery through cloud tooling.

This role emphasizes leadership, architecture, and hands-on engineering across Google Cloud, CI/CD, and data platforms.

Qualifications

• Bachelor’s degree in a quantitative field required.
• Minimum 5+ years as ML Engineer with independent project delivery.
• At least 2+ years in management or leadership.
• Deep ML Ops expertise across production ML systems and tooling.

Responsibilities

• Lead ML Ops Engineers and coach team members.
• Own ML lifecycle from ETL to model deployment in prod.
• Develop reusable frameworks and standardized ML tooling.
• Prototype cutting-edge ML infrastructure.
• Provide technical thought leadership on cloud-based ML tools.
• Document best practices and communicate with leaders.

Skills

ML Ops

Docker

Kubernetes

CI/CD

Git

API development

Model deployment

Python

TensorFlow

PyTorch

scikit-learn

Spark

Airflow

Vertex AI

BigQuery

Dataproc

Cloud infrastructure

Agile

Team leadership

Education

Bachelor’s degree in Data Science, Computer Science, Statistics, Applied Mathematics or equivalent quantitative field

Master’s Degree

Tools

Kubeflow

Airflow

Terraform

Cloud IAM

Dataproc

BigQuery

Vertex AI

GKE

Job description

Kohl's is seeking a Manager, Machine Learning Operations (ML Ops) to lead a team of engineers in designing, deploying, and operating scalable ML solutions across the MLE ecosystem. You will drive the full ML lifecycle, build ETL pipelines, deploy models to customer-facing apps, and enable efficient delivery through cloud tooling.

This role emphasizes leadership, architecture, and hands-on engineering across Google Cloud, CI/CD, and data platforms.