Miles Adkins
AI Field Engineering, Enterprise at Fireworks AI
Chicago, Illinois, United States
Summary
🤩
Rockstar🎓
Top SchoolMiles Adkins is a Solutions Architect with five years of hands-on experience architecting AI/ML platforms and go-to-market strategies for enterprise customers, most recently leading Snowflake’s AI/ML workload engagements and now supporting financial services at Databricks. He blends deep product-facing pre-sales expertise with engineering chops—contributing to the open-source Feast feature store by building Snowflake integrations, online stores, and materialization engines to operationalize features. Miles has a track record of bridging platform strategy and implementation, from deploying Snowflake Native Apps to improving connection caching and data mapping for production MLOps. His background spans trading-focused data science and customer-facing ML roles at firms like DataRobot and Imperative Execution, giving him a rare mix of quantitative modeling experience and cloud-native systems design. Based in Chicago, he excels at turning complex AI use cases into scalable, practical platform solutions that drive adoption and measurable business value.
5 years of coding experience
3 years of employment as a software developer
Bachelor of Science (B.S.), Finance (Honors), Bachelor of Science (B.S.), Finance (Honors) at Illinois State University
Stackoverflow
1reputation
0reached
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Github Skills (8)
mlops10
python10
snowflake10
data-engineering9
big-data8
data-science8
cloud-infrastructure5
infrastructure5
Programming languages (2)
Github contributions (5)
feast-dev/feast
Jan 2022 - Dec 2022
The Open Source Feature Store for AI/ML
Role in this project:
MLOps Engineer
MLOps EngineerContributions:55 reviews, 34 commits, 72 PRs in 11 months
Contributions summary:Miles primarily focused on integrating and improving the Snowflake data platform within the Feast feature store. They contributed significantly to the development and enhancement of the Snowflake integration, including the addition of an online store and materialization engine. They were responsible for bug fixes, code cleanups, and improvements to data mapping, the registry, and testing related to Snowflake's features. The user also focused on code refactoring, including the addition of connection caching.
aifeature-storemachine-learningmlbig-data
sfc-gh-madkins/feast
Feb 2022 - Jan 2024
Feature Store for Machine Learning
Contributions:3 PRs, 279 pushes, 89 branches in 1 year 11 months
feature-storemachine-learning
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