AI/ML Developer | Generative AI | NLP | Cloud Solutions
Email: [email protected]

I'm a passionate AI/ML Developer with hands-on experience in NLP-driven solutions, recommendation systems, and Generative AI. I specialize in building scalable and impactful solutions leveraging Python, TensorFlow, and cloud technologies. I am dedicated to solving real-world problems using data-driven insights and advanced AI models.

• Developing NLP-driven chatbots and Generative AI models for diverse domains.
• Enhancing my expertise in cloud deployments and advanced AI frameworks.
• Exploring cutting-edge techniques in personalized recommendation systems and data analytics.

• Programming Languages: Python (NumPy, Pandas), SQL
• Machine Learning & AI: TensorFlow, NLP (BERT, GPT), Deep Learning, Supervised/Unsupervised Learning, Clustering, Regression, Classification
• Data Science Tools: Scikit-Learn, Matplotlib, Seaborn
• Cloud & Deployment: AWS (EC2, S3), FastAPI
• Version Control: Git
• Soft Skills: Problem-Solving, Communication, Collaboration, Analytical Thinking

Radixile Solutions Technology

• Designed an AI-powered finance chatbot for the "Run My Society" platform, enabling users to query financial data (dues, payments) via NLP-driven interactions.
• Enhanced chatbot performance through advanced NLP training and seamless integration into the society management platform.
• Improved operational efficiency and user satisfaction by providing real-time insights and personalized assistance.

Yess InfoTech Private Limited, Pune

• Built and deployed data-driven solutions, focusing on extracting insights from complex datasets.
• Developed NLP models for sentiment analysis and entity recognition, improving text analytics accuracy.
• Conducted data preprocessing and created interactive dashboards using Matplotlib and Seaborn.

• Bachelor of Engineering in Computer Engineering
  Savitribai Phule Pune University
• Diploma in Computer Engineering
  Government Polytechnic, Miraj

• Machine Learning Course (Skill Up) | August 2023
• Introduction to Deep Learning | March 2024
• Data Science Course (Yess InfoTech) | March 2023
• Introduction to Generative AI (Google Cloud) | February 2024

An AI-powered finance chatbot designed to assist users with financial queries through natural language interactions.

• NLP Query Understanding: Recognized and responded to user queries about dues, payments, and transaction history.
• Real-Time Data Integration: Integrated backend systems to fetch live financial data for real-time insights.
• Transactional Assistance: Tracked payments, verified transactions, and generated financial summaries.

Developed a recommendation system for an e-commerce platform to provide personalized product suggestions.

• Data Collection: Collected and processed user interaction data (browsing, purchase history).
• Collaborative Filtering: Built user-based and item-based recommendation models.
• Matrix Factorization (SVD): Enhanced scalability and recommendation accuracy.
• Hybrid Model: Combined methods for highly relevant and personalized product recommendations.

Developed a machine learning model to predict crop yields based on environmental factors.

• Input Features: Year, rainfall, pesticide usage, temperature, and area under cultivation.
• Model: Built a regression model using Random Forest to accurately predict yield.
• Deployment: Created a web application using Flask for user-friendly interaction with the prediction model.

Designed an end-to-end NLP-powered news research tool tailored for equity research analysts.

• Features: Extracted and summarized relevant news articles using advanced NLP pipelines.
• LangChain Integration: Leveraged LangChain to connect large language models for high-context understanding.
• Outcome: Improved the research workflow with AI-driven insights for financial decision-making.

Implemented a machine learning-based fraud detection model to reduce false positives in financial transactions.

• Data Analysis: Conducted exploratory data analysis and feature engineering on transaction data.
• Model Training: Used XGBoost for classification, achieving high precision and recall.
• Deployment: Integrated the model into a production pipeline for real-time fraud monitoring.

Developed an NLP pipeline to analyze customer feedback and classify sentiments.

• Data Collection: Aggregated reviews from social media and online platforms.
• Text Preprocessing: Tokenization, stemming, and stopword removal.
• Model: Trained a sentiment classifier using BERT, achieving high accuracy on test data.

• LinkedIn: Rajesh Vhankade
• Email: [email protected]

Did you know? The term "data scientist" was coined as recently as 2008, marking the dawn of a transformative era in technology.