Armaghan Shakir
Machine Learning Engineer at German Center for Open Source AI
Islamabad, Islamabad Capital Territory, Pakistan
Summary
🤩
Rockstar🎓
Top SchoolArmaghan Shakir is a machine learning engineer based in Islamabad with four years of experience building applied ML solutions, from EEG pathology detection and recommendation systems to production pipelines for time-series forecasting. He has deep open-source experience with sktime—contributing core development, technical documentation improvements, and integrations with deep learning backends like PyTorch and Hugging Face—and authored 20+ PRs during a Google Summer of Code project. Currently at the German Center for Open Source AI, he implemented classical ML models for industry partners and built LLM/agent pipelines to automate eco-spec document workflows. A DAAD-selected research scholar with publications on knowledge distillation, he blends research rigor with practical system delivery and a track record of clear, user-focused documentation (including meticulous docstring fixes that improve code usability).
4 years of coding experience
3 years of employment as a software developer
FSc, Pre-Engineering, FSc, Pre-Engineering at Military College JhelumBachelor's degree, Computer Software Engineering, Bachelor's degree, Computer Software Engineering at National University of Sciences and Technology (NUST)
Stackoverflow
13reputation
385reached
0answers
2questions
Github Skills (14)
rs10
restructuredtext10
sktime10
python10
documentation10
time-series9
machine-learning8
google-forms6
javascript6
google-apps-script6
routes6
react-router-dom6
forms6
react6
Programming languages (3)
Github contributions (5)
sktime/sktime
Feb 2024 - Mar 2025
A unified framework for machine learning with time series
Role in this project:
Technical Writer
Technical WriterContributions:232 reviews, 31 PRs, 1 push in 1 year 1 month
Contributions summary:Armaghan primarily contributed to improving the documentation within the `sktime/sktime` repository. Their commits focus on fixing formatting errors, specifically addressing the incorrect use of backticks and single quotes in docstrings to ensure accurate rendering of code snippets. Additionally, the user added examples for the `YtoX` transformer and updated the documentation, showcasing skills in explaining and clarifying code usage, including corrections for the `NeuralForecastRNN` parameters and behaviors. The user's work is focused on enhancing the clarity, accuracy, and usability of the documentation, making the project more accessible to users.
forecastingtime-series-analysistime-series-regressiondata-sciencedeep-learning
geetu040/transformers
Nov 2024 - Mar 2025
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
Contributions:191 pushes, 5 branches in 4 months
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.