Larry Li
Solutions Architect at GDT - General Datatech
Redwood City, California, United States
Summary
👤
Senior🎓
Top SchoolLarry Li is a seasoned infrastructure and solutions architect with 8+ years in senior cloud and transformation roles and over two decades managing large-scale UNIX and datacenter environments. Based in Redwood City, he has led global cloud infrastructure teams and architected multi-cloud (AWS, Azure, GCP, IBM, Oracle) platforms, migration and SaaS operations, blending hands-on systems engineering with strategic transformation work. His background spans deep operational expertise—VMware/Cisco UCS, NetApp, elite observability stacks (Elasticsearch, Splunk), CDN and WAN optimization—to designing resilient, production-grade services for enterprise customers. At GDT he now focuses on translating complex legacy systems into cloud-forward, value-driven solutions, and he’s known for driving repeatable change management, documentation and operational excellence. A pragmatic technologist, Larry pairs low-level systems know-how with a knack for untangling messy infrastructure during large-scale cloud transitions.
8 years of coding experience
19 years of employment as a software developer
Cal Poly Pomona
English, Chinese
Stackoverflow
11reputation
2kreached
2answers
0questions
Github Skills (22)
lucene10
sparse10
retrieval10
record-linkage10
information-retrieval10
similarity10
representations10
cosine-similarity10
python9
nlp8
java7
natural-language-processing7
question-answering7
ranking7
architectures6
Programming languages (3)
Github contributions (5)
larryli1999/larryli1999.github.io
Sep 2021 - Jun 2022
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
Contributions:1 release, 66 pushes in 9 months
templategithub-pages-templatemmistakesmistakesjekyll
larryli1999/pygaggle
May 2021 - Nov 2021
a gaggle of deep neural architectures for text ranking and question answering, designed for Pyserini
Contributions:2 PRs, 20 pushes, 7 branches in 6 months
pytorchnlparchitecturesquestiondeep-learning
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