What are the responsibilities and job description for the Machine Learning Engineer II (Remote) position at Kohl's?
About The Role
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.
What You’ll Do
What Skills You Have
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.
What You’ll Do
- 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
What Skills You Have
- 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
- 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