Pranjal Tandon
Software Engineer (IoT Operations) - EShepherd
Kansas City, Missouri, United States
Summary
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Senior🎓
Top SchoolPranjal Tandon is a software engineer specializing in IoT operations with nine years of experience building firmware and connected systems, currently focused on eShepherd at Gallagher Animal Management in Kansas City. He combines embedded firmware expertise from roles at Murano and internships at Ingersoll Rand with end-to-end IoT engineering delivered over five years at Promethean Energy. Pranjal also brings hands-on machine learning experience—contributing core components to a PyTorch Soft Actor-Critic implementation—bridging control systems and reinforcement learning for real-world devices. He holds a Master’s in Computer Engineering from Arizona State University and a B.Tech in Electronics and Communications, reflecting strong theoretical grounding alongside practical product work. Known for iterating from prototype to production, he often surfaces subtle cross-domain optimizations between ML models and constrained embedded platforms. Pragmatic and curious, he blends signal-processing roots with modern ML-informed firmware to improve reliability and autonomy in deployed IoT fleets.
9 years of coding experience
6 years of employment as a software developer
Master's degree Computer Engineering, Master's degree Computer Engineering at Arizona State UniversityBachelor of Technology (B.Tech.) Electronics and Communications Engineering, Bachelor of Technology (B.Tech.) Electronics and Communications Engineering at SRM IST ChennaiApeejay School, NOIDA
Stackoverflow
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0reached
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Github Skills (5)
deep-reinforcement-learning10
pytorch10
python10
reinforcement-learning10
machine-learning9
Programming languages (2)
Github contributions (5)
pranz24/pytorch-soft-actor-critic
Aug 2018 - Sep 2021
PyTorch implementation of soft actor critic
Role in this project:
ML Engineer
ML EngineerContributions:1 review, 173 commits, 9 PRs in 3 years
Contributions summary:Pranjal implemented and updated core components of the Soft Actor-Critic (SAC) algorithm using PyTorch. Their commits demonstrate modifications to key files like `sac.py` and `model.py`, including changes to the policy, critic, and value networks, and parameter update mechanisms. The user's contributions directly relate to the training and functionality of the SAC reinforcement learning model, evidenced by the modifications to the learning process and model saving and loading.
pytorchactor-criticpytorch-implmentionreinforcement-learningdeep-reinforcement-learning
pranz24/Blynk-Water-Level-Sensor
Mar 2017 - Aug 2018
Contributions:19 commits, 18 pushes, 1 branch in 1 year 5 months
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