Muhammad Sinan

Machine Learning Engineer

Bengaluru, Karnataka, India0 mo experience

AI EnabledAI ML Practitioner

Key Highlights

Specializes in AI with hands-on full-stack experience.
Developed advanced NLP systems for emotion detection.
Focused on scalable AI solutions and clean architecture.

Stackforce AI infers this person is a Full-Stack AI Engineer with a focus on NLP and Computer Vision.

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Skills

Core Skills

Natural Language Processing (nlp)Python (programming Language)

Other Skills

scikit-learnNumPyPandasNLTKspaCyBERTStreamlitmlDeep LearningText ClassificationEnglishPostman APIData ScienceBootstrap (Framework)JavaScript

About

Hi, I'm Muhammad Sinan, a final-year BCA student specializing in Artificial Intelligence, with hands-on experience in full-stack and machine learning development. I focus on building intelligent systems using CNN-based image models, NLP-powered chatbots, and scalable AI backends. My primary tools include Python, TensorFlow, Django, React, FastAPI, SQL, and Git, with additional experience in model optimization and deployment workflows. My project portfolio reflects practical AI applications, including an E-Commerce platform, Potato Disease Prediction using a TensorFlow CNN model, Image Classification with data augmentation, House Price Prediction using structured ML pipelines, and an AI Chatbot with intent recognition and API integration. I emphasize clean architecture, optimized inference (TFLite/quantization), and seamless system integration from frontend to backend. I am driven by a continuous learning mindset and actively contribute to AI-focused development by exploring new research, attending industry workshops, and shipping real-world projects. I am currently open to AI/ML opportunities, internships, and collaborative projects where I can contribute, grow, and build meaningful AI-driven solutions.

Experience

Tcs ion

Machine Learning Engineer

Nov 2025 – Jan 2026 · 2 mos · Remote

Developed an AI-based system to automatically detect and classify human emotions from textual comments and user feedback using Natural Language Processing (NLP) techniques.
Performed text preprocessing including tokenization, stopword removal, lemmatization, and vectorization (TF-IDF / embeddings).
Built and trained machine learning models to classify emotions such as happy, sad, angry, neutral, and fear.
Evaluated model performance using accuracy, precision, recall, and F1-score, and improved results through feature engineering and hyperparameter tuning.
Implemented the solution using Python, leveraging libraries such as scikit-learn, NumPy, Pandas, and NLTK / spaCy.
Designed the system to support real-world feedback analysis, helping businesses understand customer sentiment and emotional trends.
Gained hands-on experience in end-to-end ML pipeline development, from data collection to model evaluation and deployment readiness.

Natural Language Processing (NLP)Python (Programming Language)