MLOps Engineer
Core
Building, deploying, and managing machine learning solutions at scale using AWS SageMaker, MLflow, H2O.ai, and PySpark to operationalize models in production.
Role type
Senior MLOps Engineer
Builds
Robust ML pipelines, scalable inference workloads, and automated model governance systems
Domain
Cloud-native machine learning and big data processing
Deliverable
production ML models
Required skills
AWS SageMaker, MLflow, PySpark, H2O.ai, CI/CD for ML, model monitoring, drift detection, feature engineering, AutoML, cloud infrastructure automation
Technologies
AWS SageMaker, MLflow, H2O.ai, PySpark
Responsibilities
Design end-to-end MLOps pipelines for training, validation, deployment, and retraining; Implement CI/CD processes for machine learning workflows; Automate model versioning, deployment, rollback, and governance; Develop and manage machine learning workflows using AWS SageMaker; Process and transform large-scale datasets using PySpark; Leverage H2O.ai frameworks for model development and evaluation
Seniority
Senior, hands-on IC
