Erik Bamberg
AI Product Development Lead at Kodamai
Glasgow, Scotland, United Kingdom
Summary
👤
SeniorErik Bamberg is an AI Product Development Lead and seasoned principal software engineer with over a decade of experience designing scalable systems and shipping production Java services. He combines deep Java expertise with hands-on AI/ML work—specializing in LLMs and vector databases—to drive practical, product-focused ML solutions. Erik has led and scaled backend components in prominent open-source projects like the Deep Java Library (contributing refactors, async model management and worker scaling features) while mentoring teams as a trainer and coach. His career spans senior engineering and architecture roles at J.P. Morgan and Barclays, and recent AI leadership positions that bridge research and delivery. A confident public speaker at conferences including NDC London and Google IO Extended, he brings clear technical storytelling to complex topics. Based in Glasgow, he pairs pragmatic system design with a knack for turning experimental ML into reliable production services.
10 years of coding experience
26 years of employment as a software developer
Github Skills (12)
javas10
http10
serve10
java10
api-design9
async9
restful-api9
rest-api9
asynchronous9
api-rest9
machine-learning8
ai8
Programming languages (2)
Github contributions (5)
deepjavalibrary/djl
Jan 2021 - Jul 2021
An Engine-Agnostic Deep Learning Framework in Java
Role in this project:
Back-end Developer
Back-end DeveloperContributions:23 reviews, 15 commits, 15 PRs in 6 months
Contributions summary:Erik primarily contributed to refactoring the request parsing mechanism, extracting it into a separate `RequestParser` class. They made changes to the `InferenceRequestHandler` to utilize the new `RequestParser` and updated the `ModelManager` to handle asynchronous worker status requests. The user also implemented changes for scaling up and down worker threads and configuring idle timing, modifying the `WorkerThread` class. Additionally, the user contributed to the model zoo browser and data transfer object models, making modifications to components and serializers.
pytorchmxnetcaffe2deep-learningagnostic
ebamberg/djl
Dec 2020 - Nov 2022
An Engine-Agnostic Deep Learning Framework in Java
Contributions:72 pushes, 38 branches in 1 year 11 months
mlpmxnetcaffe2deep-learningagnostic
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