AI/ML Engineer - Snowflake Cloud

Develop, train, evaluate, and deploy Machine Learning models for business use cases. Build predictive analytics, classification, regression, recommendation, and NLP solutions. Work with structured and unstructured datasets for model development and optimization. Perform feature engineering, model tuning, and performance optimization. Develop data pipelines and ETL/ELT workflows using Snowflake. Build AI/ML solutions leveraging Snowpark and Snowflake's data platform capabilities. Design and implement scalable data models and data warehousing solutions in Snowflake. Develop applications using LLMs and Generative AI technologies. Build Retrieval-Augmented Generation (RAG) pipelines integrating enterprise data sources. Implement prompt engineering and LLM evaluation techniques. Collaborate with Data Engineers, Data Scientists, and business stakeholders to deliver AI-driven solutions. Technical Requirements: 2-3 years of hands-on AI/ML development experience Strong Python and SQL programming skills Experience developing and deploying ML models using Snowflake and cloud platforms Hands-on experience with Snowflake Data Cloud, Snowpark, and data warehousing concepts Understanding of the ML lifecycle from data preparation to model deployment and monitoring Experience building and maintaining ETL/ELT pipelines and data transformation workflows Responsibilities: Develop, train, evaluate, and deploy Machine Learning models for business use cases. Build predictive analytics, classification, regression, recommendation, and NLP solutions. Work with structured and unstructured datasets for model development and optimization. Perform feature engineering, model tuning, and performance optimization. Develop data pipelines and ETL/ELT workflows using Snowflake. Build AI/ML solutions leveraging Snowpark and Snowflake's data platform capabilities. Design and implement scalable data models and data warehousing solutions in Snowflake. Develop applications using LLMs and Generative AI technologies. Build Retrieval-Augmented Generation (RAG) pipelines integrating enterprise data sources. Implement prompt engineering and LLM evaluation techniques. Collaborate with Data Engineers, Data Scientists, and business stakeholders to deliver AI-driven solutions. Preferred Skills: Technology->AI-Generative AI->Artificial Intelligence - BASIC,Technology->AI-Data science->Machine Learning,Technology->Data on Cloud-DataStore->Snowflake