MLOps Engineer

Job Title: MLOps Engineer
Location: Minneapolis, MN (Hybrid – 3 Days Onsite)
Employment Type: 6 Months Contract

Job Summary

We are seeking a skilled MLOps Engineer to join our team in Minneapolis, MN. The ideal candidate will have hands-on experience building, deploying, and maintaining scalable machine learning pipelines and production-ready ML systems.

You will work closely with data scientists, software engineers, and cloud teams to operationalize machine learning models while ensuring reliability, scalability, and performance in a Google Cloud Platform (Google Cloud Platform) environment.

Key Responsibilities

• Design, develop, and maintain scalable machine learning pipelines and workflows.
• Deploy, monitor, and maintain machine learning models in production environments.
• Collaborate with data scientists, software engineers, and business stakeholders to operationalize ML solutions.
• Build and optimize data engineering pipelines using Python.
• Implement best practices for model versioning, monitoring, governance, and CI/CD.
• Maintain and deploy model endpoints using the most efficient serving frameworks.
• Optimize cloud infrastructure and ML workflows for performance, scalability, and cost efficiency.
• Troubleshoot production issues and continuously improve ML platform reliability.
• Support end-to-end machine learning lifecycle from training through deployment and monitoring.

Required Qualifications

• 3–5 years of experience in MLOps, Machine Learning Engineering, or a related field.
• Strong proficiency in Python, with experience building data engineering pipelines.
• Hands-on experience with Google Cloud Platform (Google Cloud Platform).
• Experience working with Vertex AI and Cloud Build.
• Strong knowledge of BigQuery SQL.
• Experience with Docker and containerization technologies.
• Solid understanding of the machine learning lifecycle, including training, deployment, monitoring, and model serving.
• Experience deploying and maintaining model endpoints using optimal serving frameworks.
• Strong problem-solving and troubleshooting skills.
• Excellent communication and collaboration skills.

Preferred Qualifications

• Experience with FastAPI or similar API frameworks.
• Knowledge of batch and real-time model deployment strategies.
• Experience with Kubernetes and release management processes.
• Familiarity with CI/CD pipelines for ML applications.
• Experience monitoring production ML systems and implementing observability best practices.

Experience

• 3–5 years of relevant experience in MLOps, Machine Learning Engineering, or a related discipline.

Technical Skills

Python , Google Cloud Platform (Google Cloud Platform) , Vertex AI , Cloud Build , BigQuery SQL , Docker , Kubernetes (Preferred) , FastAPI (Preferred) , Machine Learning Model Deployment , MLOps , CI/CD , Model Monitoring , Data Engineering