Description
Job Title : ML Engineer Snowflake
Location : Chennai / Bangalore
Compensation : Open to discussion
Notice Period : Immediate joiners or candidates serving notice with 60 days
About The Role
We are seeking a skilled Machine Learning Engineer with hands-on experience in building and deploying machine learning models natively on Snowflake.
The ideal candidate will design and deliver end-to-end ML workflows using Snowpark ML and Snowflakes native model management ecosystem for large-scale enterprise use cases.
You will work closely with data engineers, analytics teams, and clients to productionize ML solutions that are scalable, secure, and performance-optimized within Snowflake.
Key Responsibilities
- Design, develop, and deploy end-to-end ML pipelines directly within Snowflake
- Build features, train models, and perform inference using Snowpark ML
- Manage model lifecycle using Snowflake Model Registry, including versioning and deployments
- Implement centralized feature management using Snowflake Feature Store
- Leverage Snowflake Cortex ML functions for use cases such as forecasting, anomaly detection, and classification
- Develop Snowpark Python UDFs and vectorized UDFs for scalable model serving
- Orchestrate ML workflows using Snowflake stored procedures
- Apply MLOps best practices including experiment tracking, monitoring, and A/B testing
- Collaborate with enterprise clients, clearly articulating technical concepts and solution designs
- Produce high-quality technical documentation and implementation guidelines
Technical Skills Required
Candidates should have hands-on experience in 68 of the following areas :
- Snowpark ML for feature engineering, model training, and inference
- Snowflake Model Registry for model versioning and deployment management
- Snowflake Feature Store for reusable and governed feature management
- Snowflake Cortex ML functions (forecasting, anomaly detection, classification)
- Snowpark Python UDFs and vectorized UDFs for model execution
- ML libraries within Snowpark : scikit-learn, XGBoost, LightGBM, PyTorch
- MLOps practices : experiment tracking, model monitoring, A/B testing
(ref:hirist.tech)
