Job Url: https://www.remoterocketship.com/company/menloinc-2/jobs/mlops-engineer-research-engineer-united-states

Job Description: 
Menlo
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Menlo is a technology company that provides products and solutions specifically designed for schools and civic organizations across the nation. Committed to improving education and community development, Menlo focuses on delivering comprehensive IT managed services for K-12 schools and innovative technology services for civic entities. 

With a strong emphasis on team culture and employee growth, Menlo seeks talented professionals to join their expanding team. 

K-12 Technology Support • 24/7 Support • Virtualization • Network Monitoring • Security


51 - 200 employees


Founded 2010

📚 Education

🏢 Enterprise

☁️ SaaS

MLOps Engineer - Research Engineer
June 14

🇺🇸 United States – Remote

⏰ Full Time

🟢 Junior

📚 Research Engineer

Airflow

Apache

AWS

Azure

Cloud

Docker

Google Cloud Platform

Kubernetes

Microservices

Python

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📋 Description
• Deploy Models: Build and deploy machine learning models, often novel architectures, into production applications, ensuring real-time performance.
• Optimize Deployment Pipelines: Design CI/CD pipelines for seamless integration and deployment of machine learning models, utilizing tools like Docker and Kubernetes for containerization
• API Integration: Create and maintain RESTful APIs or microservices to facilitate model serving and integration with existing applications.
• Continuous Monitoring: Implement monitoring solutions to track model performance and detect issues such as data drift or performance degradation, ensuring timely updates.
• Performance Optimization: Identify bottlenecks and optimize the performance of the application using best practices in Python development
• Collaboration: Work closely with the Research team to understand training, serving and optimization requirements
• Documentation: Create comprehensive documentation for code, processes, and systems to facilitate knowledge sharing within the team
• Continuous Improvement: Stay updated with industry trends and emerging technologies to continuously enhance the platform.

🎯 Requirements
• Model Serving & Deployment: Proven Experience with Docker , Kubernetes (K8s), or Seldon Core for containerization, orchestration and scaling of deployed models.
• Infrastructure Management: Manage infrastructure using Infrastructure-as-code
• Deployment experience: to cloud environments (AWS, GCP, Azure) or Self hosted On-Premise environments
• Pipeline Orchestration: Design and maintain data pipelines using Apache Airflow or Kubeflow for workflow automation
• Experiment Tracking & Model Registry: Implement and manage MLflow for tracking experiments and maintaining a model registry.

🏖️ Benefits
• 14 days leave (and unlimited sick days)
• Equipment budget