Remote GenAI Data Engineer

Sedgwick; United States; Remote; USD 140,000 - 190,000; Full time

Job summary

Sedgwick is hiring an AI Engineer to design and deliver enterprise data pipelines powering ML, LLM, and AI initiatives. You will bridge legacy systems with Snowflake, AWS, and Azure cloud ecosystems, emphasizing data quality, governance, and scalable architectures.

You will work as a day-one builder, collaborating with Data Science and MLOps teams to operationalize AI workloads across multi-cloud platforms. Remote work is available across the United States.

Qualifications

• Master's degree in Computer Science, Data Engineering, or a related field preferred.
• Six (6) years of hands-on data engineering experience building production-grade pipelines for Data Science/AI in multi-cloud environments.

Responsibilities

• Design, build, and maintain ETL/ELT pipelines from on-premises systems to cloud services.
• Develop and maintain feature stores and datasets for ML workflows.
• Engineer data pipelines for generative AI, including vectorization and embedding pipelines.
• Lead Snowflake modeling, Snowpipe automation, and compute/storage optimization.
• Create complex cross-platform data workflows with Airflow, AWS Step Functions, and Azure Data Factory.
• Partner with IT, DBAs, security teams to resolve connectivity and security issues.
• Implement data quality, validation, and observability to detect drift and anomalies.

Skills

Snowflake architecture

Python

SQL

PySpark

Docker

Airflow

AWS

Azure

Mainframe data

Data engineering

Education

Master's degree in Computer Science or Data Engineering

Tools

Snowflake

AWS

Azure

Airflow

Snowpipe

Docker

Job description

Sedgwick is hiring an AI Engineer to design and deliver enterprise data pipelines powering ML, LLM, and AI initiatives. You will bridge legacy systems with Snowflake, AWS, and Azure cloud ecosystems, emphasizing data quality, governance, and scalable architectures.

You will work as a day-one builder, collaborating with Data Science and MLOps teams to operationalize AI workloads across multi-cloud platforms. Remote work is available across the United States.