Gev Sogomonian is a co-creator and MLOps-focused software engineer with 10 years of experience building scalable ML systems and developer tools from Berkeley, California. He helped found Aim, the open-source experiment tracker, and led implementation of aim-deploy features including model wrapping, metadata handling, and Docker-based deployment to streamline moving models from research to production. Prior roles span machine learning engineering, data engineering, and frontend leadership—demonstrating fluency across full-stack product delivery and platform design. Gev has a track record of establishing AI platforms at PressReader and driving frontend architecture and componentization at Altocloud. He combines academic grounding (MS in Computer Science) with business and startup experience (MBA coursework and Berkeley SkyDeck), enabling both technical depth and product-minded execution. Notably, his contributions focus on making ML deployment practical and repeatable, reducing friction between experimentation and production at scale.
10 years of coding experience
8 years of employment as a software developer
Berkeley SkyDeck, Berkeley SkyDeck at University of California, Berkeley
MBA Business Project Management, MBA Business Project Management at Queen's University Belfast
Bachelor's degree Informatics and Applied Mathematics, Bachelor's degree Informatics and Applied Mathematics at Yerevan State University
Master of Science (MS) Computer Science; Business Management, Master of Science (MS) Computer Science; Business Management at American University of Armenia
Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.
Role in this project:
MLOps Engineer
Contributions:87 reviews, 88 commits, 54 PRs in 2 years 6 months
Contributions summary:Gev's primary contributions center around establishing an AI deployment tool, specifically for machine learning models. They initiated the project by setting up the `aim-deploy` command-line interface and configuring its core functionalities. The user then implemented model wrapping and packing mechanisms, introducing metadata handling to facilitate engine processing and model export. Subsequently, they integrated Docker deployment capabilities, enabling the creation and building of Docker images for model deployment.
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