Manager, Machine Learning Operations

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 DoAttract, 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 Required Bachelor ’s degree in Data Science, Computer Science, Statistics, Applied Mathematics or equivalent quantitative field5+ years of experience as a Machine Learning Engineer with a proven track record of successful, independent, project delivery2+ 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, Big Query 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., Tensor Flow, 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 experience7+ 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

Job Summary Job number: R507822

Profession:
Technology