Mlops Engineer - Remote Eligible

Mid To Senior Level MLOps Engineer

Menlo is looking for a mid to senior level MLOps Engineer specializing in model deployment to join our team. The ideal candidate will be responsible for deploying, managing, and optimizing machine learning models in production environments, ensuring seamless integration and efficient operations.

We believe that this role is inherently research-oriented because it bridges cutting-edge model development directly into practical, scalable solutions. Deploying novel architectures often requires deep understanding of underlying research principles, active engagement with model experimentation, and continuous innovation to meet real-world performance requirements. As such, candidates in this role naturally function as Research Engineers, closely collaborating with our research team and contributing significantly to advancing our machine learning capabilities.

Responsibilities

• 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

• See HR policies
• 14 days leave (and unlimited sick days)
• Equipment budget

* Please submit only 1 application, as you will be considered across roles. Duplicate submissions will be automatically archived.