Haris Mahmood
Founding AI Engineer at Unify
Islamabad, Islamabad Capital Territory, Pakistan
Summary
🤩
Rockstar🎓
Top SchoolHaris Mahmood is a founding AI engineer with five years’ experience building ML-driven healthcare solutions and leading early-stage AI product efforts from Islamabad. He has progressed from embedded and hardware-integrated medical projects to senior ML roles and founding positions, blending system-level engineering with applied machine learning. As CEO of Ivy and a maintainer on the ivy repository, he contributed substantive NumPy-fronted array operations that improve framework portability across ML backends. Haris favors fast-paced, collaborative environments and focuses on delivering societally valuable AI in healthcare, bringing both research-adjacent experience and hands-on production deployments. Notably, his background spans firmware and PCB QA for medical devices through to production ML pipelines, giving him rare end-to-end visibility on medical AI systems.
5 years of coding experience
4 years of employment as a software developer
A Levels, Pre-Engineering, 2A* & 1A, A Levels, Pre-Engineering, 2A* & 1A at The City School Capital Campus IslamabadBachelor of Science - BS, Electrical and Electronics Engineering, 3.69, Bachelor of Science - BS, Electrical and Electronics Engineering, 3.69 at National University of Sciences and Technology (NUST)O Levels, Sciences, 6A* & 2A, O Levels, Sciences, 6A* & 2A at The City School Rawalpindi
English, Urdu, German
Github Skills (9)
tensorrt10
web-framework10
machine-learning10
tensor10
tensorflow10
python10
operation10
numpy10
testing9
Programming languages (4)
Github contributions (5)
ivy-llc/ivy
Sep 2022 - Jan 2023
Convert Machine Learning Code Between Frameworks
Role in this project:
ML Engineer
ML EngineerContributions:298 reviews, 24 commits, 150 PRs in 3 months
Contributions summary:Haris primarily contributed to the implementation of NumPy-based array methods within the `ivy` framework, specifically for the frontend. The user added support for instance methods like `any`, `all`, `matmul`, and fixes for cases involving ellipsis, slices, and shape mismatches in operations such as transpose and broadcasting. These changes involved modifications to both test files and core NumPy-related functionalities, contributing to the framework's ability to handle various array operations.
pythontensorflowframework-learningtemplatedata-science
hmahmood24/ivy
Mar 2023 - Mar 2024
The Unified Machine Learning Framework
Contributions:45 pushes, 5 branches in 11 months
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