ML Engineer (Snowflake & MLflow)

Job Description: Must Have Skills: ML Engineer, Snowflake & MLflow External Description We are seeking an ML Engineer with strong expertise in deploying, monitoring, and managing machine learning pipelines on Snowflake. The ideal candidate will act as a bridge between the Data Engineering team and the ML team, ensuring seamless integration of ML models into production workflows. Hands-on experience with MLflow, Snowpark, and ML pipeline orchestration is essential. Deploy, monitor, and optimize ML models on Snowflake. Design and maintain end-to-end ML pipelines (training, validation, deployment, monitoring). Use MLflow for experiment tracking, model registry, and performance monitoring. Collaborate with Data Engineers on data ingestion, transformations, and model-ready datasets. Implement MLOps best practices (CI/CD for ML, model versioning, reproducibility). Support performance tuning of ML models and pipelines. Provide governance and documentation for ML workflows. ML Engineering: ML pipeline design, model deployment, monitoring, and retraining. Snowflake: Strong knowledge of Snowflake platform, Snowpark, and SQL. MLflow: Hands-on experience with experiment tracking, model registry, and monitoring. Programming: Python (pandas, numpy, scikit-learn, PySpark). DevOps/MLOps: CI/CD pipelines, version control (GitHub), containerization (Docker), orchestration (Airflow/Databricks workflows). .