[Hiring] Engineer - Gen AI @Sedgwick

Role Description

The Gen AI Engineer within the Transformation Office serves as the hands-on architect of the enterprise data supply chain for the organization’s most advanced analytics, data science, and AI initiatives. This role performs the critical engineering work required to deliver high-fidelity, production-grade data that powers machine learning models, feature stores, and generative AI applications.

• Designs, builds, and maintains resilient ETL/ELT pipelines that ingest data from on-premise systems, AWS services (S3, RDS), and Azure platforms (Blob Storage, Azure SQL).
• Develops and maintains feature stores and analytically optimized datasets that support machine learning workflows.
• Engineers data pipelines for generative AI use cases, automating the extraction, transformation, chunking, and loading of structured and unstructured data into vector databases.
• Acts as a Snowflake power user and technical lead, implementing advanced data modeling patterns and Snowpipe automation.
• Executes non-invasive data extraction strategies to unlock mission-critical data from legacy systems.
• Designs and manages complex, cross-platform data workflows using orchestration tools such as Airflow, AWS Step Functions, and Azure Data Factory.
• Partners closely with central IT, database administrators, infrastructure, and security teams to resolve connectivity and access challenges.
• Implements automated data quality, validation, and observability frameworks.
• Drives efficiency across the data ecosystem by optimizing storage, compute usage, and query performance.
• Operates as a dedicated engineering partner to MLOps, Data Science, and AI teams.

Qualifications

• Master’s degree in Computer Science, Data Engineering, or a related field from an accredited college or university preferred.
• Six (6) years of hands-on data engineering experience, with a track record of building production-grade pipelines for Data Science and AI in multi-cloud environments.
• Expert-level proficiency in Snowflake architecture, including data sharing and performance tuning.
• Advanced, hands-on knowledge of AWS (S3, Glue, Lambda) and Azure (Data Factory, Synapse) data services.
• Mastery of Python, SQL, and PySpark.
• Proven ability to interface with "old world" tech and transform it for modern cloud consumption.
• A strong understanding of the specific data needs for Machine Learning and Generative AI.
• A "get-it-done" attitude, capable of navigating enterprise bureaucracy and technical debt.
• Ability to work in a team environment.
• Ability to meet or exceed Performance Competencies.

Requirements

• Clear and conceptual thinking ability; excellent judgment, troubleshooting, problem solving, analysis, and discretion.
• Ability to handle work-related stress and multiple priorities simultaneously.
• Ability to meet deadlines.
• Computer keyboarding and travel as required.
• Hearing, vision, and talking.

Benefits

• Work-life balance.
• Caring culture.
• Recognition as a Great Place to Work®.
• Ranked in Fortune Best Workplaces in Financial Services & Insurance.