MLOps Engineer (Remote)

Role Overview

You’ll build and operate the infrastructure that moves Joveo’s machine learning models from experiment to production reliably and repeatedly. You will ensure models are versioned, monitored, and deployed with the same rigor as the company’s software systems.

Key Responsibilities

• Build and maintain end-to-end ML pipelines (training → deployment → monitoring)
• Design model registries, experiment tracking, and feature store infrastructure
• Automate model retraining, validation, and deployment workflows
• Monitor deployed models for performance drift, data skew, and prediction quality
• Collaborate with ML engineers and data scientists to streamline path-to-production
• Integrate ML infrastructure with CI/CD and cloud platforms

Required Skills & Qualifications

• Strong experience with ML platforms such as MLflow, Kubeflow, SageMaker, or Vertex AI
• Proficiency in Python and infrastructure tooling: Docker, Kubernetes, Terraform
• Experience building automated training and deployment pipelines
• Familiarity with feature stores: Feast, Tecton, or Hopsworks
• Understanding of model monitoring and drift detection approaches
• Strong DevOps/platform engineering foundations applied to the ML lifecycle

Nice to Have

• Experience integrating ML pipelines with CI/CD and multiple cloud platforms is implied by the requirements.