AI 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

By joining Sedgwick, you'll be part of something truly meaningful. It's what our 33,000 colleagues do every day for people around the world who are facing the unexpected. We invite you to grow your career with us, experience our caring culture, and enjoy work-life balance. Here, there's no limit to what you can achieve.

Newsweek Recognizes Sedgwick as America's Greatest Workplaces National Top Companies

Certified as a Great Place to Work®

Fortune Best Workplaces in Financial Services & Insurance

AI Engineer

PRIMARY PURPOSE :

The 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.

Operating as a "day‑one" builder, the Gen AI Engineer designs and delivers data pipelines that bridge legacy on‑premise systems-including mainframes, SQL Server, and DB2-with modern cloud platforms such as Snowflake and AWS/Azure AI ecosystems. The role ensures that data is not merely transferred, but deliberately engineered to meet the statistical, performance, and governance requirements of model training, inference, and RAG‑based AI systems.

Remote role, open to any U.S. state.

ESSENTIAL FUNCTIONS AND RESPONSIBILITIES

• 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), centralizing and curating data for consumption in Snowflake and downstream AI services.
• Develops and maintains feature stores and analytically optimized datasets that support machine learning workflows, ensuring data is clean, versioned, reproducible, and statistically valid for Data Science teams.
• Engineers data pipelines that enable generative AI use cases, including the automated extraction, transformation, chunking, and loading of structured and unstructured data into vector databases across AWS and Azure environments.
• Acts as a Snowflake power user and technical lead, implementing advanced data modeling patterns, Snowpipe automation, and compute and storage optimization to support high‑concurrency analytics and AI workloads.
• Executes non‑invasive data extraction strategies to unlock mission‑critical data from decades‑old legacy systems while preserving system stability and avoiding disruption to core business operations.
• Designs and manages complex, cross‑platform data workflows using orchestration tools such as Airflow, AWS Step Functions, and Azure Data Factory to ensure reliable, synchronized data movement across the organization's multi‑cloud architecture.
• Partners closely with central IT, database administrators, infrastructure, and security teams to resolve connectivity and access challenges-including PrivateLink, IAM, network segmentation, and firewall controls-while securing production approval for new data integrations.
• Implements automated data quality, validation, and observability frameworks to detect data drift, anomalies, and integrity issues that could negatively impact production analytics, machine learning, or AI systems.
• Drives efficiency across the data ecosystem by optimizing storage, compute usage, and query performance in Snowflake, AWS, and Azure, ensuring responsible cost management and measurable ROI for Transformation Office initiatives.
• Operates as a dedicated engineering partner to MLOps, Data Science, and AI teams, rapidly iterating on evolving data requirements and translating experimental use cases into scalable, production‑ready data solutions.

ADDITIONAL FUNCTIONS and RESPONSIBILITIES

• Performs other duties as assigned.
• Travel as required.

QUALIFICATIONS

Education & Licensing

Master's degree in Computer Science, Data Engineering, or a related field from an accredited college or university preferred.

Experience

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 or equivalent combination of education and experience required.

Skills & Knowledge

• Expert-level proficiency in Snowflake architecture, including data sharing, performance tuning, and the integration of Snowflake with external cloud AI services
• Advanced, hands‑on knowledge of AWS (S3, Glue, Lambda) and Azure (Data Factory, Synapse) data services
• Mastery of Python, SQL, and PySpark. Deep experience with data orchestration and containerization (Docker)
• Proven ability to interface with "old world" tech (on‑premise SQL, Mainframe extracts, flat files) and transform it for modern cloud consumption
• A strong understanding of the specific data needs for Machine Learning (feature engineering) and Generative AI (vectorization and embedding pipelines)
• A "get-it-done" attitude, capable of navigating enterprise bure