Machine Learning Engineer - Snowflake DB

Overview

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
  
  

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)