Bryant Moscon
Associate Director Of Cognitive Machine Learning - AI Engineering
Boston, Massachusetts, United States
Summary
🤩
Rockstar🎓
Top SchoolBryant Moscon is an experienced software engineer and leader with 13 years building high-performance, production-grade systems across finance, trading, cloud, and healthcare AI. He currently leads Humana's Cognitive/ML engineering organization, shipping NLP, LLM and GenAI solutions on Azure and Kubernetes while bridging research models to scalable production with tools like Databricks, Triton, and Terraform. His background spans low-latency trading systems and large-scale data infrastructure—contributions to projects like the arctic time-series store and a scalable cryptostore reveal deep expertise in storage, data pipelines, and backend systems. He teaching computer science and Python courses as a senior adjunct, which keeps his hands-on skills sharp and informs his approach to mentorship and developer productivity. Comfortable in fast-paced, high-expectation environments, he combines systems-level thinking (C/C++, networking) with modern ML engineering to deliver reliable, auditable AI solutions for regulated industries.
13 years of coding experience
16 years of employment as a software developer
BS Computer Science, BS Computer Science at The University of Texas at AustinMasters Computer Science, Masters Computer Science at Colorado State University
English, Italian
Stackoverflow
3,301reputation
197kreached
8answers
23questions
pytest
top-1%
python
top-5%
Github Skills (40)
timeseries-database10
postgresql10
pytest10
python10
redis10
pandas10
mongodb-database10
asyncio10
cryptocurrency10
arctic10
mongodb10
testing9
admin9
dockers9
kafka9
Programming languages (14)
Github contributions (5)
man-group/arctic
Nov 2015 - Dec 2020
High performance datastore for time series and tick data
Role in this project:
Back-end Developer
Back-end DeveloperContributions:2 releases, 35 reviews, 356 commits in 5 years 1 month
Contributions summary:Bryant's commits focused on enhancing the `arctic` datastore, a high-performance time series and tick data storage system. Their primary contributions involved exposing pandas metadata to the user through a `read_metadata` function, adding integration tests for pandas dataframes and series, and fixing unit tests to ensure the integrity of the system. Further work included refactoring code for improved data handling and implementing new APIs to expose information about stored data, including segment count and size.
time-seriestickstoremongodbpandasarctic
bmoscon/cryptostore
Jan 2019 - Jan 2023
A scalable storage service for cryptocurrency data
Role in this project:Back-end Developer
Back-end DeveloperContributions:18 releases, 66 reviews, 317 commits in 4 years
Contributions summary:Bryant's contributions primarily focused on developing core backend functionalities for the cryptocurrency data storage service. They implemented a configuration loading mechanism, including a YAML-based configuration file and dynamic reloading capabilities using asyncio. Furthermore, the user worked on building a collector interface for interacting with cryptocurrency data feeds. They also added support for using redis streams and arctic for data storage.
cryptocurrencypythondocker-containercryptofeedpostgresql
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