Prakhar Gupta
Consultant at Deloitte
Gurugram, Haryana, India
Summary
🤩
Rockstar🎓
Top SchoolPrakhar Gupta is a Consultant with nine years of engineering experience, specializing in Salesforce integration and CLM-ERP connectivity while blending practical ML and data-science skills from academic and internship projects. Based in Gurugram, he brings a civil-engineering foundation and hands-on research experience from IIT Delhi and ISM Dhanbad where he applied clustering, Random Forest and XGBoost to geotechnical and LIDAR problems. At Deloitte he has progressed from Analyst to Consultant, delivering enterprise integrations that bridge business processes and technical systems. His open-source contributions to EPFL’s OptML and sent2vec projects show solid ML and NLP chops—implementing optimization algorithms, tokenization for real-world text, and efficiency-minded quantization. That mix of integration engineering, applied ML, and domain research gives him a pragmatic edge in turning complex data workflows into production-ready solutions.
9 years of coding experience
1 year of employment as a software developer
High School Diploma, 93,25% (ISC 2018), High School Diploma, 93,25% (ISC 2018) at Seth M. R. Jaipuria School LucknowBachelor's degree, Civil Engineering, Bachelor's degree, Civil Engineering at Malaviya National Institute of Technology Jaipur
Github Skills (16)
text-processing10
data-preprocessing10
tokenize10
machine-learning10
tokenizer10
nlp10
gradient-descent10
python10
numpy10
fasttext10
data-science9
optimization9
jupyter-notebook9
algorithms9
quantization8
Programming languages (3)
Github contributions (5)
epfml/sent2vec
Mar 2017 - Sep 2021
General purpose unsupervised sentence representations
Role in this project:
ML Engineer
ML EngineerContributions:1 release, 25 commits, 5 PRs in 4 years 6 months
Contributions summary:Prakhar primarily contributed to the development and enhancement of sentence embedding capabilities within the sent2vec repository. Their work included the addition of tokenization scripts for tweets and wiki data, which are critical for processing text data. Further contributions included code for sentence similarity, sentence analogies, and the incorporation of quantization techniques to potentially optimize model performance and efficiency. This shows an understanding of core NLP concepts and model optimization techniques.
nlpsentencerepresentationsword-embeddingsunsupervised
epfml/OptML_course
Mar 2018 - Jul 2018
EPFL Course - Optimization for Machine Learning - CS-439
Role in this project:ML Engineer
ML EngineerContributions:11 commits, 11 pushes in 3 months
Contributions summary:Prakhar primarily contributed to an iPython notebook within an EPFL course on Optimization for Machine Learning. The commits focus on implementing and testing machine learning algorithms, specifically Least Squares Estimation and Stochastic Gradient Descent. The user worked with loading and preparing data for these algorithms, and implemented key functions such as computing stochastic gradients and performing gradient descent. The user was also involved in visualization of the learning process.
optimizationmachine-learningepfl
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.