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.

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