Prabhsimran Singh
Senior System Software Engineer at NVIDIA
Bengaluru, Karnataka, India
Summary
🤩
Rockstar🎓
Top SchoolPrabhsimran Singh is a Senior System Software Engineer based in Bengaluru with nine years of experience building production-grade ML and speech systems, currently at NVIDIA after recent senior ML roles at Observe.AI and Skit.ai. He combines deep practical expertise in reinforcement learning and seq2seq architectures—evidenced by contributions to popular repos implementing DQN/DDQN and attention-based translation—with hands-on experience shipping real-time, scalable inference pipelines for conversational AI. At Skit.ai he led R&D on speech recognition and task-oriented dialog, and earlier work at Vahan improved fraud detection and intent classification in production. Curious and relentless by his own GitHub bio, he often focuses on pragmatic algorithmic fixes (loss functions, replay buffers, training loops) that yield measurable performance gains. He balances research-minded experimentation with production rigor, making him effective at taking ML prototypes through to robust deployment.
9 years of coding experience
5 years of employment as a software developer
The Lawrence School - Sanawar - IndiaBachelor of Technology Computer Science, Bachelor of Technology Computer Science at Manipal University Jaipur
Hindi, English, French
Stackoverflow
31reputation
2kreached
2answers
1question
Github Skills (30)
pytorch10
trading-algorithms10
neural-machine-translation10
python10
attention-mechanism10
machine-learning10
seq2seq10
reinforcement-learning10
keras10
deep-learning10
tensorflow10
nlp10
chatbot9
data-structures8
data-structure8
Programming languages (15)
Github contributions (5)
pskrunner14/trading-bot
Aug 2018 - Jan 2020
Stock Trading Bot using Deep Q-Learning
Role in this project:
ML Engineer
ML EngineerContributions:31 commits, 26 pushes, 1 branch in 1 year 5 months
Contributions summary:Prabhsimran primarily contributed to the development of a stock trading bot leveraging Deep Q-Learning. Their commits focused on implementing the core agent logic, including action selection, reward calculations, and experience replay. They also made several changes to the model architecture and hyperparameters to improve performance. The user saved model checkpoints and added evaluation routines.
stock-marketstock-tradingpythontrading-botbacktesting-trading-strategies
tensorlayer/seq2seq-chatbot
Aug 2018 - Oct 2018
Chatbot in 200 lines of code using TensorLayer
Role in this project:ML Engineer
ML EngineerContributions:17 commits, 4 PRs, 23 comments in 1 month
Contributions summary:Prabhsimran primarily focused on refactoring and improving the codebase for a seq2seq chatbot built using TensorLayer. Their contributions included fixing deprecated issues related to global variable initialization, enhancing code readability, and refactoring the inference mode. They also made changes to the data loading and preprocessing steps and updated the model training and inference functionalities within the main script.
tensorlayerlineslines-of-codetensorflowchatbot
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.