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 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