Kurai

Join our ML Infrastructure team as an MLOps Engineer where you’ll build the pipelines and platforms that deploy, monitor, and scale ML models from research to production. You’ll bridge the gap between data science and engineering, automating model training, feature engineering, and deployment workflows. Our models serve millions of predictions daily with sub-100ms latency requirements. You’ll work with Kubeflow, MLflow, and custom tooling to make MLOps seamless for our team.

Responsibilities

• Design and maintain CI/CD pipelines for ML models using GitHub Actions, ArgoCD, and custom tooling
• Build and operate ML platforms on Kubernetes with GPU acceleration (NVIDIA, AWS EKS)
• Implement feature stores (Feast) and data versioning (DVC, Delta Lake) for reproducible ML
• Monitor model performance in production with drift detection, A/B testing, and automated retraining
• Optimize inference latency through model quantization, ONNX, TensorRT, or custom serving solutions
• Manage ML experiment tracking with MLflow or Weights & Biases; ensure reproducibility
• Collaborate with data scientists to productionize research code and establish best practices
• Implement automated testing for data quality, model validation, and pipeline integrity

Qualifications

• 4+ years of DevOps/MLOps experience with 2+ years specifically in ML infrastructure
• Strong Python skills; experience with ML frameworks (PyTorch, TensorFlow, scikit-learn)
• Production experience with Kubernetes, Docker, and GPU orchestration
• Deep understanding of ML lifecycle: training, validation, deployment, monitoring, retraining
• Experience with cloud platforms (AWS SageMaker, GCP Vertex AI, or Azure ML)
• Familiarity with feature stores, experiment tracking, and ML metadata systems
• Infrastructure-as-Code skills: Terraform, CloudFormation, or Pulumi
• Experience with monitoring: Prometheus, Grafana, DataDog, or CloudWatch
• BS/MS in CS, Engineering, or related field; experience at ML-focused companies is a plus