Manager, Machine Learning Operations

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

About The Role

As Manager, Machine Learning Operations (ML Ops), you will manage a team of engineers in designing, deploying, and operating machine learning solutions while building scalable infrastructure, tools, and best practices across the Machine Learning Engineering (MLE) ecosystem.

What You’ll Do

• Attract, retain, develop, manage, coach and assess ML Ops Engineers
• Lead engineers on implementation of the full ML lifecycle, including building and scaling extract, transform, and load (ETL) pipelines, deploying Data Scientist developed models into customer-facing applications, and enabling efficient organizational throughput through cloud infrastructure and tooling
• Own the roadmap for Machine Learning Engineering and Data Science tools, including developing reusable frameworks and standardized solutions to streamline model implementation
• Research, prototype and instruct the team on creating cutting edge machine learning infrastructure
• Enable change for the team by moving decisions forward and eliminating blockers
• Provide technical thought leadership for Data Science managers on the topics of cloud-based tools and infrastructure for the ML lifecycle
• Instill best practices for your team and foster a culture of continuous learning and development, while training and coaching them through pair programming and code review
• Contribute to development, monitoring, alerting, and automated testing frameworks to ensure the reliability, performance, and integrity of data pipelines, models, and infrastructure
• Document, and communicate implementations and best practices to other leaders across Kohl’s broader data organization
• Stay current with Google Cloud Product (GCP) services and evolving best practices in MLE and ML Ops
• Additional tasks may be assigned

What Skills You Have

• Bachelor’s degree in Data Science, Computer Science, Statistics, Applied Mathematics or equivalent quantitative field
• 5+ years of experience as a Machine Learning Engineer with a proven track record of successful, independent, project delivery
• 2+ years of managerial or leadership experience in Data Science or Analytics organizations
• Expertise in ML Ops practices, including building and operating production ML systems using Docker, Kubernetes, CI/CD pipelines, Git-based version control, API development, model serving (batch and real-time), and automated testing frameworks
• Experience working with Data Scientists to deploy, scale, and operationalize machine learning models in production environments
• In-depth knowledge of cloud platform, preferably Google Cloud Platform services, particularly Vertex AI, BigQuery and Dataproc.
• Extensive expertise with CI/CD and IaC best practices
• Extensive knowledge of distributed computing and big data technologies like Spark, Kubeflow, Airflow and SQL
• Extensive expertise in Python and machine learning libraries (e.g., TensorFlow, PyTorch, scikit-learn)
• Experience working in Agile environments with an emphasis on iterative development and continuous delivery

Preferred

• Master’s Degree
• Proficiency in Java or other languages
• Retail and E-Commerce experience
• 7+ years of experience in Machine Learning
• Experience with optimization techniques and tools (e.g., Gurobi, linear programming, mixed-integer programming)
• Experience working with agent based or agentic AI systems, including orchestration of autonomous workflows or LLM-driven agents