Top Remote Artificial Intelligence Data Engineering Jobs

As a Data Architect at RapidScale, you'll lead data platform engagements, ensuring delivery quality, managing teams, and designing cloud architectures while supporting ML implementations and client interactions.

As a Data Architect, you will lead end-to-end data and ML platform engagements, manage delivery teams, design cloud architectures, and support presales activities.

The Data Architect will lead data platform engagements including execution, architecture design, team coordination, and client relationships, providing expertise in cloud data systems and machine learning infrastructure.

The Sr. Specialist executes data integration and platform requirements in Marketing, supporting large-scale data initiatives and documentation processes alongside cross-functional teams.

The Epic Caboodle Database Administrator will manage Epic Caboodle and Cogito databases, ensuring performance, stability, and data readiness for analytics, while also supporting operational databases.

The Data & Analytics Development Program offers recent graduates accelerated development through 3 rotations in different areas, focusing on data science, data engineering, and analytics projects.

As a Data Engineer II, you will build and optimize data pipelines in Databricks, ensure high-quality data delivery, and support generative AI applications. You will collaborate with data scientists and AI engineers to provide reliable data infrastructures and meet business needs.

The Data Engineer will design and build scalable data systems, support AI/ML integration, and collaborate across teams for the development of Canoe's Alts Data Platform.

Lead the ML Data Enablement team to define strategy and execution for accelerating third-party and internal data onboarding, enforce data quality and SLAs, build metadata and lineage standards, and deliver scalable data infrastructure and workflows to reduce time-to-production for ML models.

The Senior Data Engineer will own and evolve data pipelines, collaborate with teams for data models and pipelines, optimize ETL processes, and implement data storage solutions.

Lead design and implementation of shared, reusable analytics data models and pipelines. Drive standardization, governance, observability, and CI/CD for analytics; partner with Data Science, Infrastructure, and Product to certify metrics, modernize orchestration, and integrate AI-native tooling.

Lead design and implementation of scalable, HIPAA-compliant data pipelines and platforms for healthcare ML. Build ETL, orchestration, and tooling for processing EHR, claims, pharmacy, and bioinformatics data; collaborate with data scientists to produce modeling-ready datasets and ensure data quality, reliability, and operational excellence.