Siraj Raval
Freelance AI ML Engineer at Siraj Raval
United States
Summary
🤩
Rockstar🎓
Top SchoolSiraj Raval is a freelance AI/ML engineer with 14 years of experience specializing in production-ready MLOps, generative AI/LLMs, and real-time computer vision systems for retail, health tech, and e-commerce. He combines Silicon Valley software engineering chops with a track record of building end-to-end pipelines—containerized APIs, automated CI/CD, and cloud deployments—that have measurably reduced costs and latency for clients. His projects include YOLOv9-powered in-store analytics and RAG-based customer support agents that cut response times by up to 70%, and he routinely applies quantization and hardware-specific tuning to achieve up to 3× inference speedups. A prolific educator and open-source contributor, he’s shared deployment tutorials and even contributed backend fixes to notable projects like ipfs/kubo, reflecting both practical production focus and community-minded engineering.
14 years of coding experience
11 years of employment as a software developer
Computer Science, Computer Science at Columbia University
Github Skills (21)
python10
machine-learning10
ipfs10
network-programming10
flask-ask10
mask-rcnn10
keras10
go10
tensorflow10
neural-network10
faster-rcnn10
flask10
command-line-interface9
continuous-deployment9
ml-deployment9
Programming languages (16)
Github contributions (5)
llSourcell/how_to_deploy_a_keras_model_to_production
Jun 2017 - Jun 2017
This is the code for the "How to Deploy a Keras Model to Production" by Siraj Raval on Youtube
Role in this project:
ML Engineer
ML EngineerContributions:11 commits, 9 pushes, 1 branch in 1 day
Contributions summary:Siraj primarily contributed to developing a Keras-based model for handwritten digit recognition within a Flask web application. Their work involved creating, training, and saving a convolutional neural network (CNN) model using the MNIST dataset. Furthermore, the user implemented the necessary logic to load and utilize the trained model within the Flask application, demonstrating skills in both model development and deployment. The user also updated the Flask application to handle user input and make predictions.
pythonkeras-modeldeep-learningmachine-learningyoutube
ipfs/kubo
Sep 2014 - Sep 2014
An IPFS implementation in Go
Role in this project:Backend Developer
Backend DeveloperContributions:14 commits, 1 comment in 19 days
Contributions summary:Siraj primarily contributed to the backend logic and functionality of the IPFS implementation in Go. Their work involved refactoring code, particularly naming conventions, and fixing uppercase issues. They also implemented and tested the `ipfs bootstrap` command, demonstrating an understanding of network configuration within the IPFS project.
golangipfsp2porbitdblibp2p
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