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Build and maintain cloud-native data pipelines and warehouses to feed AI-driven automotive analytics and marketing tools for U.S. dealerships.
Principal ML Engineer designs, builds, and deploys production-grade AI systems using LLMs and deep learning, integrating agent workflows across GCP, AWS, and Azure.
Design and build custom software solutions using modern frameworks and agile practices, with a focus on Snowflake Data Warehouse development and optimization.
Build and maintain scalable data pipelines, cloud data warehouses, and real-time processing systems using Python, SQL, and cloud platforms to power analytics and business decisions.
Build and scale a data-driven cybersecurity analytics platform that ingests vulnerability data, processes it efficiently, and delivers actionable insights to customers.
Design and implement cloud security architectures, automate controls, and secure AI/ML platforms across AWS and Azure while embedding security into CI/CD and MLOps pipelines.
Designs and builds scalable data pipelines and backend services to ingest, process, and manage data for U.S. federal agencies using Python/Node.js, APIs, and ETL workflows.
Build and improve AI-powered tax assistant features, RAG, generative AI, workflow automation, and fiscal insights using Python, backend services, and AI orchestration frameworks.
Build and deploy scalable data pipelines and workflows for healthcare payor/provider systems, migrating legacy warehouses to cloud and ensuring data quality and governance.
Build and maintain scalable data pipelines using dbt and Fivetran to transform and integrate enterprise data for analytics and reporting.
Designs and builds large-scale data pipelines and warehouses for Amazon’s payment products, enabling analytics and reporting on financial transactions and services.
Build and harden enterprise data products in Snowflake and dbt, using SQL and Python to deliver governed, scalable datasets for analytics, AI-enabled apps, and ML pipelines at a cybersecurity company.
Design and operate Capital Group’s enterprise AI platform, building scalable vector databases, RAG pipelines, agent frameworks, and AI Gateways to securely deploy generative AI and agentic solutions across the firm.
Design and maintain scalable data pipelines to ingest HR and workforce data (especially from Workday) into a cloud data platform using Fivetran, APIs, and file-based methods, ensuring reliability for downstream analytics.
Design and build enterprise-grade MLOps/LLMOps platforms on AWS SageMaker and EKS, automating model lifecycle from training to monitoring and ensuring scalable, secure AI deployments.
Lead the design and integration of Generative AI features—like RAG pipelines and agent workflows—into M&T Bank’s secure systems using Java, Python, or C#.
Build and maintain scalable data pipelines for finance metrics using Python, Airflow, dbt, and Snowflake to support CrowdStrike’s AI-native cybersecurity platform.
Who We Are Verily Health is a data platform and technology company purpose-built to power AI-enabled precision health solutions that accelerate research and improve care for individuals and communities. Uniquely…
Build and maintain cloud-native data pipelines on AWS, including API ingestion, ETL with Lambda/Glue, Redshift modeling, and dashboards for business stakeholders.
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