Chetan Gulecha
Senior Machine Learning Engineer at Qualcomm
Hyderabad, Telangana, India
Summary
🤩
Rockstar🎓
Top SchoolChetan Gulecha is a Senior Machine Learning Engineer based in Hyderabad with three years of industry experience focused on model optimization and quantization. At Qualcomm he progressed from intern to senior engineer, contributing practical improvements that bridge research techniques and production needs. His open-source work on AIMET highlights expertise in TensorFlow quantization, per-layer sensitivity analysis, and Keras Adaround optimizations—skills that reduce model size while preserving accuracy. A strong academic background with a CS master's from IIIT Bangalore and a BTech from COEP complements his hands-on engineering approach. Colleagues would note his knack for turning nuanced numerical analysis into actionable tooling for efficient deployment.
3 years of coding experience
4 years of employment as a software developer
Bachelor of Technology - BTech, Computer Engineering, 8.72/10 CGPA, Bachelor of Technology - BTech, Computer Engineering, 8.72/10 CGPA at College of Engineering PuneH.S.C., 91.23%, H.S.C., 91.23% at Vasantrao Naik College, AurangabadMaster's degree, Computer Science, 3.5/4 CGPA, Master's degree, Computer Science, 3.5/4 CGPA at International Institute of Information Technology BangaloreS.S.C., 95.82%, S.S.C., 95.82% at St. Lawrence Semi English School, Aurangabad
English, Marwari, Marathi, Hindi
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (13)
net10
quantization10
quants10
machine-learning10
lossless-compression10
deep-learning10
tensorflow10
compress10
python9
compression9
keras9
pruning8
deep-neural-networks8
Programming languages (1)
Github contributions (2)
quic/aimet
Oct 2022 - Dec 2022
AIMET is a library that provides advanced quantization and compression techniques for trained neural network models.
Role in this project:
ML Engineer
ML EngineerContributions:6 commits, 45 PRs, 18 pushes in 2 months
Contributions summary:Chetan contributed significantly to the AIMET repository, focusing on enhancing the TensorFlow quantization analyzer. Their work involved implementing features for per-layer min-max range and PDF analysis, crucial for understanding model sensitivity. The user's commits also include optimizations for the Keras Adaround technique and performed per-op sensitivity analysis by enabling/disabling quant ops, further improving model efficiency. The contributions focused on model quantization and analysis, demonstrating proficiency in deep learning optimization techniques.
pytorchtechniquesdeep-learningpruningcompression
quic-cgulecha/aimet
Sep 2022 - Feb 2025
AIMET is a library that provides advanced quantization and compression techniques for trained neural network models.
Contributions:1 push, 53 branches in 2 years 4 months
pytorchtechniquesdeep-learningpruningcompression
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