Machine Learning Engineer II (Remote)

Kohl's•Tallahassee, FL

1d•Remote

About The Position

In this role, you will focus on MLOps, supporting cross-functional teams in designing, deploying, and operating machine learning solutions while building scalable infrastructure, tools, and best practices across the Machine Learning Engineering (MLE) ecosystem.

Requirements

Experience in MLOps or DevOps 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
Bachelor’s degree in Data Science, Computer Science, Statistics, Applied Mathematics or equivalent quantitative field
Experience working with Data Scientists to deploy, scale, and operationalize machine learning models in production environments
3+ years of experience as a Machine Learning Engineer with a proven track record of successful project delivery
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

Nice To Haves

Master’s Degree
Proficiency in Java or other languages
Retail experience
E-commerce experience
5+ 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

Responsibilities

Collaborate with Data Scientists and Engineers across the full ML lifecycle, including building and scaling ETL pipelines, deploying models into customer-facing applications, and enabling efficient model development through cloud infrastructure and tooling
Design, build, and maintain scalable machine learning infrastructure, including model serving (real-time and batch), training environments, and orchestration systems, with a focus on performance, scalability, and cost efficiency
Contribute to the roadmap for Machine Learning Engineering and Data Science tools, including developing reusable frameworks and standardized solutions to streamline model implementation
Partner with and support Data Scientists by enabling effective use of cloud-based tools and infrastructure, and providing technical expertise across the ML lifecycle
Collaborate with machine learning engineers to share knowledge, improve best practices, and foster a culture of continuous learning and development
Support development and maintain monitoring, alerting, and automated testing frameworks to ensure the reliability, performance, and integrity of data pipelines, models, and infrastructure
Develop, document, and communicate implementations and best practices across the data science lifecycle
Manage and communicate cloud infrastructure costs and budgets to project stakeholders
Stay current with GCP services and evolving best practices in Machine Learning Engineering and MLOps
Additional tasks may be assigned