Rizwan Gilani
Engineering Manager at Meta
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolRizwan Gilani is an engineering manager based in Palo Alto with a decade of experience across applied ML modeling, ML infrastructure, and large-scale systems engineering. He has led full-stack teams at Amazon’s SageMaker and now drives AI/ML work at Meta/PyTorch, specializing in democratizing ML through Data Wrangler, Canvas, and LLM/GenAI integrations. Comfortable bridging research and production, he has shipped core ML tooling, negotiated strategic partner integrations (Snowflake, Databricks, Salesforce), and managed cross-org technical execution. A former Alexa engineer and early founder, he pairs product-minded leadership with hands-on systems design and a background in academic research from the University of Toronto. Notably, he has delivered tooling for preprocessing massive point-cloud datasets and led a digital-preservation initiative that secured significant international funding.
7 years of coding experience
10 years of employment as a software developer
Bachelor of Science (BS), Computer Science, Bachelor of Science (BS), Computer Science at Lahore University of Management SciencesMaster’s Degree, Applied Computing, Master’s Degree, Applied Computing at University of Toronto
English, Urdu, Punjabi
Github Skills (23)
docker-container10
aws10
reinforcement-learning9
xgboost9
python9
sagemaker9
machine-learning9
inference9
huggingface9
amazon-sagemaker9
jupyter-notebook9
deep-learning8
distributed-training8
gbm8
data-science8
Programming languages (4)
Github contributions (5)
rizwangilani/ml-io
Dec 2019 - Oct 2021
A high performance data access library for machine learning tasks
Contributions:10 PRs, 33 pushes, 17 branches in 1 year 10 months
data-sciencedata-accessdata-access-librarymlmachine-learning
rizwangilani/sagemaker-xgboost-container
Nov 2019 - Apr 2020
This is the Docker container based on open source framework XGBoost (https://xgboost.readthedocs.io/en/latest/) to allow customers use their own XGBoost scripts in SageMaker.
Contributions:7 PRs, 22 pushes, 7 branches in 4 months
sagemakerxgboostdeep-learningcustomersdocker
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