Remote AI Data Engineer for GenAI & ML Pipelines

Sedgwick; Nashville (TN); Remote; USD 140,000 - 210,000; Full time

Job summary

Sedgwick seeks an AI Engineer to architect and deliver enterprise data pipelines for advanced analytics, ML, and generative AI. You will bridge legacy systems with Snowflake on AWS/Azure, create feature stores, and ensure data quality for production AI workloads.

As a hands-on engineer, you will lead data modeling, Snowpipe automation, and cost-optimized compute strategies while collaborating with security and central IT teams across the multi-cloud landscape.

Qualifications

• Master's degree preferred in Computer Science, Data Engineering, or related field.
• 6+ years hands-on data engineering with production-grade pipelines for Data Science/AI.
• Expert Snowflake architecture, performance tuning, and Snowflake–external services integration.
• Strong Python, SQL, and PySpark proficiency; experience with data orchestration and containers.
• Experience bridging legacy on-prem systems with cloud data platforms across multi-cloud environments.

Responsibilities

• Design and implement resilient ETL/ELT pipelines from on-premise systems to Snowflake and AI services.
• Build and maintain feature stores and clean, versioned analytics datasets for ML workflows.
• Develop data pipelines for generative AI use cases, including vectorization and embedding pipelines.
• Collaborate with IT, security, and DBAs to secure data access and governance across multi-cloud stacks.

Skills

Snowflake

AWS

Azure

Python

SQL

PySpark

Data pipelines

Docker

Airflow

MLOps

Education

Master's in CS or Data Eng

Tools

Snowflake

Airflow

AWS

Azure Data Factory

Job description

Sedgwick seeks an AI Engineer to architect and deliver enterprise data pipelines for advanced analytics, ML, and generative AI. You will bridge legacy systems with Snowflake on AWS/Azure, create feature stores, and ensure data quality for production AI workloads.

As a hands-on engineer, you will lead data modeling, Snowpipe automation, and cost-optimized compute strategies while collaborating with security and central IT teams across the multi-cloud landscape.