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 Required • 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