ML Ops Engineer (USA / Israel)

5+ years of experience in ML Ops, owning ML infrastructure for large-scale systems. Strong coding, debugging, performance analysis, testing, and CI/CD discipline. Extensive commercial experience with Python developing automated pipelines. Production experience on AWS, DataBricks, Docker + Kubernetes (EKS/ECS or equivalent). Experience with Terraform or CloudFormation for IaC. Experience with MLflow/SageMaker (or similar) and production ML pipelines. Experience with ML monitoring and pipeline alerting. Experience with PySpark/Glue/Dask/Kafka for large-scale batch/stream processing. Familiarity with model serving patterns and feature stores. Exposure to model governance/compliance and secure ML operations.

Productionize training and inference for ML models. Establish CI/CD for models, data/versioning, and model governance. Centralize feature generation and manage model registry/metadata. Implement monitoring for data quality, drift, model performance, and pipeline health. Refactor research code into reusable components and enforce best practices. Collaborate with data scientists, analysts, and engineers. Drive technical vision for ML platform capabilities and establish architectural patterns.