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Shubhamm Dhadse

Machine Learning & Generative AI Engineer | AI Agents | RAG | FastAPI | SQL I Data Science & Data Analyst

Bhopal

< 1 year of work experience

AI Varient
Jun 20257 mosDec 2025

Hyderabad, Telangana, India

• Developed an end-to-end Sentiment Analysis pipeline including data cleaning, tokenization, stopword removal, lemmatization, and TF–IDF vectorization. • Trained and optimized a Linear SVM model to classify customer reviews with strong accuracy and reliable performance. • Created visualizations (word clouds, frequency plots) to extract insights from text sentiment patterns. • Deployed the model using Streamlit, enabling real-time sentiment prediction through a clean UI. • Project Link: View Project • Certificate: View Certificate

Education

Technocrats Institute of Technology and Science

B.Tech(CSE)

Technocrats Institute of Technology and Science

20214 yrs2025

Bachelor of Technology - BTech, Computer science and engineering

TIT Group

20214 yrs2025

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Headline

Machine Learning & Generative AI Engineer | AI Agents | RAG | FastAPI | SQL I Data Science & Data Analyst

About

Aspiring Generative AI / ML Engineer and Data Scientist with hands-on experience building end-to-end ML models, RAG pipelines, LLM-powered applications, and multi-agent AI systems. Skilled in Python, FastAPI, Machine Learning, Statistics, Data Analysis, and modern GenAI frameworks, with practical experience in data preprocessing, predictive modeling, vector database integration, and scalable AI solution development. Experienced in designing intelligent workflows, automating processes, and developing AI-driven applications to solve real-world problems. Passionate about leveraging machine learning, large language models, and data-driven insights to build impactful and scalable solutions while continuously learning advanced AI and ML technologies.

Location

Bhopal

Work experience

Data Science Intern at AI Varient, Jun 2025 - Dec 2025 · 7 mos, Hyderabad, Telangana, India, • Developed an end-to-end Sentiment Analysis pipeline including data cleaning, tokenization, stopword removal, lemmatization, and TF–IDF vectorization. • Trained and optimized a Linear SVM model to classify customer reviews with strong accuracy and reliable performance. • Created visualizations (word clouds, frequency plots) to extract insights from text sentiment patterns. • Deployed the model using Streamlit, enabling real-time sentiment prediction through a clean UI. • Project Link: View Project • Certificate: View Certificate

Education

B.Tech(CSE), Technocrats Institute of Technology and Science

Bachelor of Technology - BTech, Computer science and engineering, TIT Group