Machine Learning Engineer

Local to Minneapolis, Minnesota

Position Overview

We are seeking a Machine Learning Engineer to design, deploy, and maintain scalable machine learning solutions in production environments. The ideal candidate will bridge the gap between data science and software engineering by transforming ML models into reliable, high-performance applications.

Key Responsibilities

• Develop and deploy production-ready machine learning services and workflows from data science models and prototypes.
• Build scalable training and inference pipelines with strong focus on code quality, reproducibility, version control, and automated validation.
• Design and optimize real-time and batch model serving architectures to meet performance and scalability requirements.
• Implement and manage MLOps tools for model tracking, versioning, deployment automation, and operational monitoring.
• Partner with Data Engineering teams to develop feature engineering pipelines and establish reliable data integration processes.
• Monitor production ML systems, identify model drift and performance degradation, and implement mitigation and recovery strategies.
• Support incident management, root-cause analysis, and continuous improvement of deployed ML solutions.

Required Qualifications

• 5 years of software engineering experience, including at least 2 years of deploying and supporting machine learning models in production environments.
• Strong programming expertise in Python.
• Hands-on experience with machine learning frameworks such as TensorFlow and PyTorch.
• Experience building and deploying containerized applications using Docker and Kubernetes.
• Knowledge of RESTful API development and integration.
• Solid understanding of machine learning system architecture, including model drift, data leakage prevention, and training-serving consistency.
• Experience implementing CI/CD pipelines and DevOps practices within ML development lifecycles.