Yugal Jain
Artificial Intelligence Engineer at ValueSoft Info Services
Rohtak, Haryana, India
Summary
👤
Senior🎓
Top SchoolYugal Jain is an Artificial Intelligence Engineer with 8 years of hands-on experience designing and deploying end-to-end NLP, deep learning, and data engineering solutions for enterprise and public-sector clients. He has built production-grade RAG chatbots, document-processing pipelines, AIOps systems, and multimodal content-moderation tools that combine vision, speech, and text to reduce legal risk and improve safety. Comfortable across the ML lifecycle, he leverages PyTorch, Transformers, ElasticSearch, Docker, and cloud services (AWS/Azure) to deliver scalable, low-latency endpoints and real-time inference. His work spans practical outcomes—doubling conversion rates for sales platforms, halving costs for multilingual translation, and achieving industry-grade classification accuracy—while also advancing research-grade models like compact toxic-comment classifiers and character-based translation systems. An active contributor to open-source tooling, he has extended cross-framework ML utilities and improved documentation and GPU-detection ergonomics in community repos. Based in Rohtak, India, he pairs deep technical breadth with a pragmatic focus on deployability and bias-aware model design.
8 years of coding experience
5 years of employment as a software developer
Bachelor of Technology, Computer Science Engineering, Bachelor of Technology, Computer Science Engineering at Guru Gobind Singh Indraprastha University
English, Hindi
Stackoverflow
1reputation
0reached
1answer
0questions
Github Skills (9)
ivy10
machine-learning10
python10
numpy10
documentation9
tensorflow7
pytorch7
jupyter-notebook6
tf-idf6
Programming languages (6)
Github contributions (5)
ivy-llc/ivy
May 2022 - Aug 2022
Convert Machine Learning Code Between Frameworks
Role in this project:
ML Engineer
ML EngineerContributions:6 reviews, 9 commits, 8 PRs in 3 months
Contributions summary:Yugal primarily contributed to the project by adding functional and instance method examples to the documentation of machine learning functions. They also added new functionalities like the `sqrt` function to the numpy frontend, expanding the project's capabilities. Furthermore, the user made multiple code reformatting commits, specifically related to determining GPU availability, ensuring code readability. They also addressed documentation issues in the gradients and manipulation modules, including optimization functions and dimension permutations, for better usability.
pythontensorflowframework-learningtemplatedata-science
yugaljain1999/ivy
May 2022 - May 2023
The Unified Machine Learning Framework
Contributions:42 pushes, 10 branches in 11 months
pythondata-sciencedeep-learningmachine-learningframework-learning
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