MLOps Engineer II: Scale ML Infra & Pipelines

FashionUnited; Menomonee Falls (WI); Remote; USD 90,000 - 120,000; Full time

Job summary

FashionUnited is seeking a Machine Learning Engineer to design and maintain scalable machine learning infrastructure. Collaborate with Data Scientists to deploy and operationalize models, ensuring reliability and performance.

The ideal candidate has a Bachelor’s degree in a quantitative field and 3+ years experience in machine learning. Familiarity with cloud platforms, especially Google Cloud, is preferred. Opportunities for continuous development and improvement are encouraged.

Qualifications

• 3+ years of experience as a Machine Learning Engineer with a proven track record of successful project delivery.
• Experience working in Agile environments with an emphasis on iterative development.
• Extensive experience with distributed computing and big data technologies.

Responsibilities

• Collaborate with Data Scientists to scale ML models in production environments.
• Design and maintain scalable machine learning infrastructure.
• Manage cloud infrastructure costs and budgets.

Skills

Experience in MLOps or DevOps practices

Building and operating production ML systems using Docker

CI/CD pipelines

API development

Model serving (batch and real-time)

Automated testing frameworks

In-depth knowledge of GCP services

Extensive expertise in Python and ML libraries

Education

Bachelor’s degree in Data Science, Computer Science, or related field

Tools

Docker

Kubernetes

Git

Spark

Airflow

TensorFlow

PyTorch

Job description

FashionUnited is seeking a Machine Learning Engineer to design and maintain scalable machine learning infrastructure. Collaborate with Data Scientists to deploy and operationalize models, ensuring reliability and performance.

The ideal candidate has a Bachelor’s degree in a quantitative field and 3+ years experience in machine learning. Familiarity with cloud platforms, especially Google Cloud, is preferred. Opportunities for continuous development and improvement are encouraged.