Looks like you have JavaScript disabled. For the full Twine experience, you will need to re-enable it.

Hi, I’m Advait – an AI Developer & Research Engineer specializing in Machine Learning, Deep Learning, NLP, and Audio Data Processing. I bring expertise in crafting custom AI solutions for real-world problems, from passion projects…Hi, I’m Advait – an AI Developer & Research Engineer specializing in Machine Learning, Deep Learning, NLP, and Audio Data Processing. I bring expertise in crafting custom AI solutions for real-world problems, from passion projects to deep learning solutions and end-to-end NLP pipelines. With a strong academic foundation and practical experience, I deliver high-quality, innovative solutions tailored to your needs.

Advait Karnatak

AI Developer, AI Engineer, Data Scientist





Hi, I’m Advait – an AI Developer & Research Engineer specializing in Machine Learning, Deep Learning, NLP, and Audio Data Processing. I bring expertise in crafting custom AI solutions for real-world problems, from passion projects to deep learning solutions and end-to-end NLP pipelines. With a strong academic foundation and practical experience, I deliver high-quality, innovative solutions tailored to your needs.Hi, I’m Advait – an AI Developer & Research Engineer specializing in Machine Learning, Deep Learning, NLP, and Audio Data Processing. I bring expertise in crafting custom AI solutions for real-world problems, from passion projects to deep learning solutions and end-to-end NLP pipelines. With a strong academic foundation and practical experience, I deliver high-quality, innovative solutions tailored to your needs.WWWWHi, I’m Advait – an AI Developer & Research Engineer specializing in Machine Learning, Deep Learning, NLP, and Audio Data Processing. I bring expertise in crafting custom AI solutions for real-world problems, from passion projects to deep learning solutions and end-to-end NLP pipelines. With a strong academic foundation and practical experience, I deliver high-quality, innovative solutions tailored to your needs.

Available to hire

Hi, I’m Advait – an AI Developer & Research Engineer specializing in Machine Learning, Deep Learning, NLP, and Audio Data Processing. I bring expertise in crafting custom AI solutions for real-world problems, from passion projects to deep learning solutions and end-to-end NLP pipelines.

With a strong academic foundation and practical experience, I deliver high-quality, innovative solutions tailored to your needs.

Skills

Github

Experience Level

Logic Pro

Expert

Github

Expert

Python

Expert

TensorFlow

Expert

Java

Expert

Keras

Expert

Large Language Model

Expert

AI Chatbot

Expert

AI Collection (Audio)

Expert

Data Science

Expert

AI Collection (Video)

Expert

AI Data Labelling

Expert

C++

Expert

Docker

Expert

Kubernetes

Expert

Language

English

Advanced

Hindi

Fluent

Education

Bachelor of Technology at IIT Delhi

October 31, 2022 - May 15, 2026

Qualifications

Advanced Learning Algorithms

May 16, 2024 - July 5, 2024

Learnt about neural networks, layers, forward propagation, activation function, softmax regression, multi-class and muli-label classification, bias, variance, transfer learning and decision trees. Worked on evaluation and improving ML models. Diagnosed bias and variance by splitting the dataset into training, cross-validation and testing sets. Countered underfitting and overfitting. Did a neural networks project for implementing binary(0,1) and multiclass(0-9) classification for handwritten numerals. Implemented decision trees in a mushroom classification project. Used entropy, data-splitting and best information gain concepts to build the decision tree. Learning and doing projects on Deep Learning which include neural networks, hyperparameter tuning, regularization, optimization, CNNs and sequence models.

Fundamentals Of Quantitative Modelling

March 24, 2025 - March 24, 2025

UPenn(Coursera) Learnt about optimization, growth, probabilistic models, Bernoulli distribution, binomial distribution, regression models.

Supervised Machine Learning

October 18, 2023 - October 30, 2023

Learnt about the fundamentals of Machine Learning including linear regression, gradient descent, feature scaling, normalization, regularization and classification from logistic regression. Did 2 regression projects :- 1) Predicted profits for a restaurant franchise by linear regression using gradient descent 2) Using logistic regression for binary classification predicting students’ admission based on marks scored(Further used regularization and feature mapping to get better classification results)

Industry Experience

Software & Internet, Professional Services, Computers & Electronics, Media & Entertainment

LyricsAnalyzer - Song Analysis with NLP

LyricsAnalyzer is a powerful tool that leverages Natural Language Processing (NLP) to analyze song lyrics. The project focuses on extracting valuable insights from lyrics, including sentiment analysis, genre prediction, and thematic categorization. By applying machine learning algorithms, the tool can classify songs based on mood, style, and genre, making it ideal for music classification, recommendation systems, and music analysis projects. Key Features: Sentiment Analysis: Understand the emotional tone of the lyrics (positive, negative, neutral). Genre Prediction: Classify songs into various genres based on their lyrics. Theme Detection: Identify common themes like love, social issues, etc., from lyrics. Data Visualization: Visualize analysis results through charts and graphs for easy interpretation. The project is built using Python and libraries like NLTK, Scikit-learn, and TensorFlow. It combines the power of text processing and deep learning techniques to provide accurate and insightful predictions. LyricsAnalyzer is ideal for music-related businesses, content creators, and researchers who need to gain deeper insights into song lyrics or develop recommendation systems based…LyricsAnalyzer is a powerful tool that leverages Natural Language Processing (NLP) to analyze song lyrics. The project focuses on extracting valuable insights from lyrics, including sentiment analysis, genre prediction, and thematic categorization. By applying machine learning algorithms, the tool can classify songs based on mood, style, and genre, making it ideal for music classification, recommendation systems, and music analysis projects. Key Features: Sentiment Analysis: Understand the emotional tone of the lyrics (positive, negative, neutral). Genre Prediction: Classify songs into various genres based on their lyrics. Theme Detection: Identify common themes like love, social issues, etc., from lyrics. Data Visualization: Visualize analysis results through charts and graphs for easy interpretation. The project is built using Python and libraries like NLTK, Scikit-learn, and TensorFlow. It combines the power of text processing and deep learning techniques to provide accurate and insightful predictions. LyricsAnalyzer is ideal for music-related businesses, content creators, and researchers who need to gain deeper insights into song lyrics or develop recommendation systems based on lyrics.WWWWWWWWLyricsAnalyzer is a powerful tool that leverages Natural Language Processing (NLP) to analyze song lyrics. The project focuses on extracting valuable insights from lyrics, including sentiment analysis, genre prediction, and thematic categorization. By applying machine learning algorithms, the tool can classify songs based on mood, style, and genre, making it ideal for music classification, recommendation systems, and music analysis projects. Key Features: Sentiment Analysis: Understand the emotional tone of the lyrics (positive, negative, neutral). Genre Prediction: Classify songs into various genres based on their lyrics. Theme Detection: Identify common themes like love, social issues, etc., from lyrics. Data Visualization: Visualize analysis results through charts and graphs for easy interpretation. The project is built using Python and libraries like NLTK, Scikit-learn, and TensorFlow. It combines the power of text processing and deep learning techniques to provide accurate and insightful predictions. LyricsAnalyzer is ideal for music-related businesses, content creators, and researchers who need to gain deeper insights into song lyrics or develop recommendation systems based on…

AI DeveloperAI EngineerData CollectorData ScientistData VisualizerDatabase Developer

InspireAI - Integrated Recommendation System

As part of my work with InspireAI, I integrated a comprehensive end-to-end recommendation system designed to deliver personalized content based on users' web-scraped LinkedIn profiles. The system was built to help users discover articles and insights aligned with their professional interests and career goals. Key Contributions: LinkedIn Profile Scraping: Developed a system to scrape and extract relevant data from LinkedIn profiles, ensuring rich user information for recommendations. NLP Pre-processing & Word2Vec: Created a robust NLP pipeline to process user profile data, followed by Word2Vec vectorization for converting textual data into meaningful word embeddings. Clustering & Recommendations: Applied K-Means clustering to categorize similar posts and articles, enabling the system to recommend personalized, contextually relevant content to users. Key Features: Personalized Content: Recommends articles, posts, and insights based on users' professional background and interests. Scalable Model: Built for scalability, capable of handling large datasets and evolving user preferences. This recommendation engine effectively uses advanced NLP techniques and machine learning to…As part of my work with InspireAI, I integrated a comprehensive end-to-end recommendation system designed to deliver personalized content based on users' web-scraped LinkedIn profiles. The system was built to help users discover articles and insights aligned with their professional interests and career goals. Key Contributions: LinkedIn Profile Scraping: Developed a system to scrape and extract relevant data from LinkedIn profiles, ensuring rich user information for recommendations. NLP Pre-processing & Word2Vec: Created a robust NLP pipeline to process user profile data, followed by Word2Vec vectorization for converting textual data into meaningful word embeddings. Clustering & Recommendations: Applied K-Means clustering to categorize similar posts and articles, enabling the system to recommend personalized, contextually relevant content to users. Key Features: Personalized Content: Recommends articles, posts, and insights based on users' professional background and interests. Scalable Model: Built for scalability, capable of handling large datasets and evolving user preferences. This recommendation engine effectively uses advanced NLP techniques and machine learning to enhance the user experience, providing tailored recommendations for career growth and industry insights.WWWWWWWWAs part of my work with InspireAI, I integrated a comprehensive end-to-end recommendation system designed to deliver personalized content based on users' web-scraped LinkedIn profiles. The system was built to help users discover articles and insights aligned with their professional interests and career goals. Key Contributions: LinkedIn Profile Scraping: Developed a system to scrape and extract relevant data from LinkedIn profiles, ensuring rich user information for recommendations. NLP Pre-processing & Word2Vec: Created a robust NLP pipeline to process user profile data, followed by Word2Vec vectorization for converting textual data into meaningful word embeddings. Clustering & Recommendations: Applied K-Means clustering to categorize similar posts and articles, enabling the system to recommend personalized, contextually relevant content to users. Key Features: Personalized Content: Recommends articles, posts, and insights based on users' professional background and interests. Scalable Model: Built for scalability, capable of handling large datasets and evolving user preferences. This recommendation engine effectively uses advanced NLP techniques and machine learning to enhan…

AI DeveloperAI EngineerData CollectorData Visualizer

Deep Learning Music Analysis and Classification

AI-Powered Audio Analysis This project focuses on accurately classifying songs into their respective genres using advanced Deep Learning and Audio Signal Processing techniques. It combines cutting-edge technology and creativity to deliver reliable music categorization, ideal for applications in music streaming, curation, and recommendation systems. Key Highlights: Audio Feature Extraction: Leveraged tools like Librosa to extract key audio features such as Mel spectrograms, MFCCs, and spectral contrast. Deep Learning Models: Implemented CNNs ,RNNs and LSTMs to capture both spatial and sequential data in audio signals. Multi-Genre Classification: Achieved high accuracy across multiple genres, ensuring scalability for large datasets. Custom Pipelines: Built robust pre-processing pipelines for audio cleaning, normalization, and augmentation to enhance model performance. Real-World Applications: Can be adapted for music discovery platforms, personalized playlists, and artist trend analysis.AI-Powered Audio Analysis This project focuses on accurately classifying songs into their respective genres using advanced Deep Learning and Audio Signal Processing techniques. It combines cutting-edge technology and creativity to deliver reliable music categorization, ideal for applications in music streaming, curation, and recommendation systems. Key Highlights: Audio Feature Extraction: Leveraged tools like Librosa to extract key audio features such as Mel spectrograms, MFCCs, and spectral contrast. Deep Learning Models: Implemented CNNs ,RNNs and LSTMs to capture both spatial and sequential data in audio signals. Multi-Genre Classification: Achieved high accuracy across multiple genres, ensuring scalability for large datasets. Custom Pipelines: Built robust pre-processing pipelines for audio cleaning, normalization, and augmentation to enhance model performance. Real-World Applications: Can be adapted for music discovery platforms, personalized playlists, and artist trend analysis.WWWWWWWWAI-Powered Audio Analysis This project focuses on accurately classifying songs into their respective genres using advanced Deep Learning and Audio Signal Processing techniques. It combines cutting-edge technology and creativity to deliver reliable music categorization, ideal for applications in music streaming, curation, and recommendation systems. Key Highlights: Audio Feature Extraction: Leveraged tools like Librosa to extract key audio features such as Mel spectrograms, MFCCs, and spectral contrast. Deep Learning Models: Implemented CNNs ,RNNs and LSTMs to capture both spatial and sequential data in audio signals. Multi-Genre Classification: Achieved high accuracy across multiple genres, ensuring scalability for large datasets. Custom Pipelines: Built robust pre-processing pipelines for audio cleaning, normalization, and augmentation to enhance model performance. Real-World Applications: Can be adapted for music discovery platforms, personalized playlists, and artist trend analysis.

AI DeveloperAI EngineerData Scientist

Hire a AI Developer

We have the best ai developer experts on Twine. Hire a ai developer in New Delhi today.

Find a AI Developer

AI Developers for hire in New Delhi, India

AI Engineers for hire in New Delhi, India

Data Scientists for hire in New Delhi, India