Machine Learning Engineer - Snowflake DB

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