Haider Sultan
AI Engineer at Deriv
Sepang, Selangor, Malaysia
Summary
🤩
Rockstar🎓
Top SchoolHaider Sultan is an AI Engineer with six years of experience building production-grade ML systems and scalable AI products across startups and cloud environments. He led the development and deployment of a high-traffic chatbot and an internal AI framework at Crewlogix, architecting end-to-end pipelines that combine NLP, embeddings, vector stores, and MLOps. At Unify and in open-source work (notably contributions to the ivy project converting ML code between frameworks), he focused on backend-agnostic tooling and framework interoperability that smooths cross-platform model development. Comfortable across computer vision, NLP, generative AI and reinforcement learning, he blends research-driven experimentation with practical engineering to deliver reliable real-time endpoints. Based in Malaysia and trained at UET Lahore, Haider is as at home mentoring teams and shaping product strategy as he is implementing stateful FFT layers and pooling functions in ML libraries.
6 years of coding experience
3 years of employment as a software developer
Bachelor's degree Computer Science, Bachelor's degree Computer Science at University of Engineering and Technology, Lahore
Stackoverflow
3reputation
51reached
0answers
1question
Github Skills (11)
neural-network10
paddlepaddle10
ivy10
pytorch10
machine-learning10
deep-learning10
tensorflow10
python10
converter9
jax8
option-type6
Programming languages (5)
Github contributions (5)
ivy-llc/ivy
Jun 2023 - May 2024
Convert Machine Learning Code Between Frameworks
Role in this project:
ML Engineer
ML EngineerContributions:109 reviews, 127 PRs, 71 pushes in 10 months
Contributions summary:Haider primarily contributed to the implementation of machine learning functionalities within the `ivy` repository. Their work involved adding and testing methods for the `paddle` frontend, including functions like `allclose`, `any`, and `astype`. Further contributions include adding and testing `dropout2d`, and the implementation of the stateful FFT layer, showing active participation in extending the framework's capabilities. The user also worked on the implementation and testing of multiple functions related to Paddle's pooling functionalities, such as `adaptive_avg_pool1d`, `adaptive_avg_pool2d` and `max_unpool1d`.
pythontensorflowframework-learningtemplatedata-science
HaiderSultanArc/AI-Insights
Apr 2020 - Jan 2021
Contributions:35 commits, 32 pushes, 1 branch in 8 months
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