Happy Shandilya
Software Engineer at Adobe
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolHappy Shandilya is a seasoned software engineer with 15 years of experience building scalable cloud-native systems and microservices, currently contributing at Adobe from San Francisco. He has a strong full-stack and backend background across Java, Node.js, Spring, AWS, Kubernetes, Docker and infrastructure tools like Terraform, with hands-on experience migrating monoliths to microservices and operating distributed data stores. Happy also contributes to open-source AI tooling, adding dataset integrations and processing pipelines to Ludwig—demonstrating an appetite for ML infrastructure beyond his day-to-day product work. Educated with an MS in Information Systems, he blends academic rigor with pragmatic delivery, often tackling data workflows and platform reliability challenges that sit at the intersection of engineering and ML.
15 years of coding experience
6 years of employment as a software developer
The University of UtahBachelor of Technology - BTech, ECE, A+, Bachelor of Technology - BTech, ECE, A+ at Amity University Jaipur
Github Skills (13)
deep-learning10
python10
data-science10
pytorch9
machine-learning8
natural-language-processing8
computer-vision7
fine-tuning6
ml5
llm5
mistral5
neural-network5
llama5
Programming languages (6)
Github contributions (5)
ludwig-ai/ludwig
Oct 2020 - Dec 2021
Low-code framework for building custom LLMs, neural networks, and other AI models
Role in this project:
ML Engineer
ML EngineerContributions:149 reviews, 6 commits, 20 PRs in 1 year 2 months
Contributions summary:Happy primarily contributes to the development of the Ludwig datasets API. Their work focuses on creating a base class for datasets, implementing download, processing, and loading functionalities. They have added implementations for specific datasets like OhsuMed and Reuters, demonstrating an understanding of data processing pipelines and how to integrate various dataset types with the Ludwig framework. They also implemented the MNIST dataset workflow, expanding dataset support for the framework.
fairness-mlpythonframework-learningdeep-learning-frameworknatural-language-processing
ludwig-ai/model-hub
Apr 2021 - Nov 2022
Contributions:2 reviews, 21 commits, 9 PRs in 1 year 7 months
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