Top Remote Artificial Intelligence Data Engineering Jobs

Design and implement real-time and batch data pipelines (MQTT/Redpanda, Dagster), parse binary message formats, build data warehouses (Postgres, Iceberg, Parquet, S3), design high-performance data models, validate and normalize sources, and improve local development and CI/CD with GitHub Actions to support maritime domain awareness and defense-related AI workloads.

The Data Infrastructure Engineer will centralize data capabilities, improve data pipeline architecture, and ensure efficient data access across teams while optimizing GCP usage.

Design, build, and maintain scalable data pipelines and tooling to support AI and analytics. Manage datastores (MySQL, Redshift, OpenSearch), implement AWS-based ETL/ELT solutions, collaborate with data science and product teams, and ensure data quality, automation, and production reliability.

The Engagement Manager will oversee the data modernization lifecycle, coordinating with partners to implement modern data platforms and manage multiple concurrent engagements effectively.

As an AI Engineer, you'll work closely with customers to understand their needs, train and deploy models, and build scalable AI systems.

Design, build, and operate GTM data pipelines, a unified data lake, and enrichment workflows using cloud data infra, LLMs, and orchestration tools to enable segmentation, ICP scoring, and revenue analytics.

Lead risk modeling initiatives from data exploration to deployment, defining methodologies, advising clients, and integrating models into platforms.

The Senior Quantitative Analyst is responsible for building data pipelines, conducting Monte Carlo simulations, and converting complex Excel models to code while communicating effectively with stakeholders.

Lead the Core Analytics team, manage data engineering standards, design analytics architectures, and mentor team members while ensuring data integrity and analytics effectiveness.

As the Senior Data Engineer for Enrichment, you'll design data pipelines, maintain contact datasets, ensure data quality, and optimize vendor relationships while working with a talented team.