j
jamesyaeger670

James Y

@jamesyaeger670

Bulk AI Data and Compute Pipeline Specialist

Vereinigte Staaten
Englisch
Einige Informationen werden in englischer Sprache angezeigt.
Über mich
I build and ship production software by directing AI coding agents as my primary implementation tool. My expertise lies in system-level thinking, breaking complex problems into buildable pieces, and orchestrating AI to ship software faster than traditional methods. I have 20+ actively deployed web applications architected and debugged by me.... Mehr lesen

Kompetenzen

j
jamesyaeger670
James Y
offline • 

Meine Dienstleistungen

Automatisierungen
I will bulk extract and process data

Portfolio

Arbeitserfahrung

Google_Developers

Founder & Lead Systems Engineer

Google Developers • Selbstständig

Apr 2025 - Present1 yr 3 mos

Designed and deployed automated multimodal AI data extraction pipelines that convert unstructured PDFs, invoices, and bank statements into structured datasets with high schema accuracy. Built serverless batch inference workflows on cloud infrastructure (Azure / Lambda GPUs), reducing processing turnaround times by over 90% compared to manual data entry. Integrated automated Pydantic schema validation and multi-format export engines delivering data in Excel (.xlsx), CSV, JSON, and SQL formats.

NVIDIA

Principal Infrastructure Developer

NVIDIA • Selbstständig

Aug 2023 - Sep 20241 yr 1 mo

Built an open-source AI orchestration framework featuring intelligent multi-model task decomposition and automated document parsing. Implemented structured JSON output validation and data cleaning pipelines to handle high-volume text and visual inputs reliably. Optimized API gateway throughput and asynchronous execution protocols to handle multi-file batch jobs efficiently.

Vacasa

Database Optimization Consultant

Vacasa • Freiberufler

Jan 2021 - Dec 202111 mos

Identified performance bottlenecks in large-dataset queries and proposed more efficient retrieval strategies to improve system scalability. Focused on closing the gap between functional SQL and high-performance SQL for large-scale production environments.