Hagay Lupesko
Senior Vice President, AI Inference at Cerebras Systems
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolHagay Lupesko is a senior technology leader with 20+ years of engineering and management experience and a decade focused on AI, currently serving as Senior Vice President of AI Inference at Cerebras Systems. He has built and scaled teams and products across startups and hyperscalers—leading AI engineering at Meta, founding key platforms at MosaicML (acquired by Databricks), and shaping deep learning tooling at AWS. Known for turning research-grade models into production-grade systems, he combines hands-on DevOps experience (notably contributing to model serving and deployment workflows in the awslabs/multi-model-server project) with executive strategy. His background spans embedded vision and medical imaging to cloud-scale ML infrastructure, giving him a rare end-to-end view of algorithm, systems, and operations. Based in San Francisco, he holds an MSc in Computer Science from Tel Aviv University and consistently prioritizes cost-effective, high-performance inference as AI moves to production. Colleagues describe him as a pragmatic builder who can navigate both hardware-aware optimization and large org execution.
10 years of coding experience
18 years of employment as a software developer
MSc Computer Science, MSc Computer Science at Tel Aviv UniversityBSc Computer Science, BSc Computer Science at Ben-Gurion University of the Negev
English, Hebrew
Stackoverflow
368reputation
4kreached
8answers
1question
Github Skills (17)
docker10
dockers10
cicd10
gunicorn9
nginx9
python8
deep-learning8
ai8
inference8
html6
mediasource6
audio6
neural-network6
mxnet6
machine-learning6
Programming languages (11)
Github contributions (5)
awslabs/multi-model-server
Oct 2017 - Jun 2018
Multi Model Server is a tool for serving neural net models for inference
Role in this project:
DevOps Engineer
DevOps EngineerContributions:2 releases, 26 commits, 16 PRs in 8 months
Contributions summary:Hagay primarily focused on updating and refining the project's build and deployment process. Their contributions involved modifying Docker configurations for both CPU and GPU environments, updating the install scripts to include necessary packages, and configuring the Nginx setup. Additionally, the user bumped the project's version in setup.py and updated the model server configuration file, highlighting their role in managing the overall deployment lifecycle. The user also fixed a multi-file download issue.
pytorchmxnetservingdeep-learninginference
lupesko/incubator-mxnet
Aug 2017 - Jan 2019
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
Contributions:8 pushes, 11 branches in 1 year 4 months
pythonschedulerdataflowmutationorchestration
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