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ML Ops Infrastructure Engineer

📅 Apr 6
MlopsDevOpsInfrastructure EngineeringPython

📜 Description

  • Design and build CI/CD pipelines specifically tailored for ML model development, validation, and deployment
  • Architect and maintain model deployment pipelines that move models from research environments through staging to production with confidence
  • Build A/B testing infrastructure that enables controlled rollouts of new models and measures real-world performance impact
  • Implement comprehensive monitoring for model performance in production -- accuracy metrics, latency, drift detection, and regression alerts
  • Develop automated retraining pipelines that trigger on data changes, performance degradation, or scheduled cadences
  • Create and maintain build and test environments that mirror production, giving researchers high-fidelity feedback before deployment

🛠️ Requirements

  • 4+ years of experience in MLOps, DevOps, or infrastructure engineering with a focus on ML systems
  • Strong proficiency in Python and experience building automation and tooling for ML workflows
  • Deep experience with CI/CD systems and building pipelines for software and model delivery
  • Hands-on experience with Docker and Kubernetes for containerized workload management
  • Practical experience deploying and serving ML models in production environments
  • Familiarity with model evaluation, validation, and quality assurance processes
  • Understanding of monitoring and observability principles as applied to ML systems
  • Strong problem-solving skills and a bias toward automation over manual processes
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