Sai Gajula

Senior Machine Learning Engineer at Qualcomm

Hyderabad, Telangana, India
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Summary

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Sai Gajula is a Senior Machine Learning Engineer based in Hyderabad with two years of hands-on experience building ML solutions at Qualcomm, progressing from intern to senior engineer within three years. Trained with a dual BTech+MTech in Computer Science from IIT Kharagpur, he blends strong academic foundations with practical experience in NLP and software engineering, having also served as a teaching assistant for NLP and data structures courses. At Qualcomm he has moved quickly through roles, signaling an ability to translate research-oriented techniques into production-ready models and systems. Sai's background suggests a talent for mentoring and communicating technical ideas to students and peers, and a preference for applied ML problems in industry settings. Although early in his career, his rapid progression and IIT research exposure indicate strong potential for driving ML projects from prototype to deployment.
code2 years of coding experience
job3 years of employment as a software developer
bookIntermediate MPC, Intermediate MPC at Sri Chaitanya College of Education
bookDual Degree (BTech + MTech) Computer Science and Engineering, Dual Degree (BTech + MTech) Computer Science and Engineering at Indian Institute of Technology, Kharagpur
languagesEnglish, Hindi, Telugu, German
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Github Skills (9)

compression10
quantization10
pruning10
machine-learning10
deep-learning10
pytorch9
open-source8
neural-network8
deep-neural-networks8

Programming languages (1)

Python

Github contributions (2)

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quic-gsaichai/aimet

Oct 2023 - Mar 2025

AIMET is a library that provides advanced quantization and compression techniques for trained neural network models.
Contributions:18 pushes, 34 branches in 1 year 4 months
quic/aimet

Sep 2023 - Mar 2025

AIMET is a library that provides advanced quantization and compression techniques for trained neural network models.
Contributions:38 PRs, 16 pushes, 1 comment in 1 year 5 months
pytorchtechniquesdeep-learningpruningcompression
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