Alay Shah
Software Engineer II at Audible
New York City Metropolitan Area United States
Summary
🤩
Rockstar🎓
Top SchoolAlay Shah is a Software Engineer II with nine years of experience building scalable, revenue-driving systems at Audible and contributing to open-source ML infrastructure like FedML for federated learning. He blends a strong academic foundation in Computer Science and Finance (Rutgers, 3.98 GPA) with hands-on experience in API design, AWS CDK deployments, A/B testing, and developer platform improvements that helped unlock over $50M in listener revenue. Comfortable across backend, mobile, and ML workflows, he has implemented federated segmentation trainers and aggregator improvements in FedML while also learning iOS development to prototype product ideas end-to-end. A former MIT COVID-19 Challenge winner and hackathon champion, he brings a problem-first mindset and interdisciplinary background in medical science that informs pragmatic, user-focused engineering.
9 years of coding experience
2 years of employment as a software developer
Bachelor of Science - BS, Computer Science, 3.98/4.0, Bachelor of Science - BS, Computer Science, 3.98/4.0 at Rutgers University - New BrunswickHigh School Diploma, High School Diploma at Egg Harbor Township High School
Github Skills (10)
pytorch10
machine-learning10
deeplearning-ai10
image-segmentation10
distributed-training10
deep-learning10
segmentation10
federated-learning10
python10
mlops8
Programming languages (7)
Github contributions (5)
FedML-AI/FedML
Nov 2020 - Mar 2021
FEDML - The unified and scalable ML library for large-scale distributed training, model serving, and federated learning. FEDML Launch, a cross-cloud scheduler, further enables running any AI jobs on any GPU cloud or on-premise cluster. Built on this library, TensorOpera AI (https://TensorOpera.ai) is your generative AI platform at scale.
Role in this project:
ML Engineer
ML EngineerContributions:67 reviews, 61 commits, 170 PRs in 3 months
Contributions summary:Alay primarily focused on implementing and refactoring components related to federated segmentation within the FEDML framework. Their contributions included adding APIs for distributed federated segmentation, refactoring the segmentation trainer to support customized model training, and integrating model training with feature extraction. The user also made modifications to the core trainer, aggregator and client manager to incorporate and streamline the segmentation task and improve the model saving procedure. The commits reveal work on a model trainer, model aggregator, and model saving strategies.
inference-enginepythonsimulationdecentralizedsilo
FedML-AI/doc.fedml.ai
Nov 2023 - Oct 2024
Contributions:25 reviews, 103 PRs, 95 pushes in 11 months
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