Gen AI Engineer - Data Supply Chain

About the role

Sedgwick is hiring a Gen AI Engineer within its Transformation Office, working remotely from Texas. The role is the hands-on architect of the enterprise data supply chain for advanced analytics, data science and AI initiatives, delivering production-grade data for machine learning models, feature stores and generative AI applications. It bridges legacy on-premise systems (mainframes, SQL Server, DB2) with modern cloud platforms such as Snowflake and AWS/Azure.

Key responsibilities

• Build and maintain resilient ETL/ELT pipelines ingesting data from on-premise systems, AWS (S3, RDS) and Azure (Blob Storage, Azure SQL), centralizing it in Snowflake for AI services.
• Develop feature stores and analytically optimized datasets that are clean, versioned and reproducible for Data Science teams.
• Engineer pipelines for generative AI, including automated extraction, transformation, chunking and loading of structured and unstructured data into vector databases on AWS and Azure.
• Act as a Snowflake power user and technical lead, implementing data modeling, Snowpipe automation and compute/storage optimization.
• Extract data from legacy systems without disrupting core operations, and manage cross-platform workflows with Airflow, AWS Step Functions and Azure Data Factory.
• Work with IT, database, infrastructure and security teams on connectivity, access and security issues, and implement data quality, validation and observability frameworks.
• Optimize storage, compute and query performance in Snowflake, AWS and Azure, and partner with MLOps, Data Science and AI teams.

Required profile

• Master's degree in Computer Science, Data Engineering or a related field preferred.
• Six years of hands-on data engineering experience building production-grade pipelines for Data Science and AI in multi-cloud environments, or an equivalent combination of education and experience.
• Ability to work in a team, handle stress and multiple priorities, and meet deadlines.

Required skills

• Expert Snowflake architecture, including data sharing, performance tuning and integration with cloud AI services.
• Advanced hands-on knowledge of AWS (S3, Glue, Lambda) and Azure (Data Factory, Synapse).
• Mastery of Python, SQL and PySpark, and data orchestration and containerization with Docker.
• Experience with on-premise SQL, mainframe extracts and flat files, and understanding of ML feature engineering and GenAI vectorization pipelines.