🔍 DQLabs.ai

🔷 Role: Deep Learning Engineer | Machine Learning Engineer

📅 Duration: [~2020]

Engineered advanced semantic type detection systems for an augmented data quality platform, enabling organizations to measure, monitor, and improve data quality through machine learning and natural language processing.

🚀 Core Contributions

🧠 Semantic Type Detection Engine

Advanced Algorithm Implementation: Developed state-of-the-art semantic type detection using Sherlock and Sato algorithms
Multi-Feature Architecture: Created system leveraging paragraph-to-vector, word embeddings, bag-of-characters, and bag-of-words vectors
High-Accuracy System: Achieved 90%+ accuracy for column type detection across diverse datasets
Continuous Learning: Built mechanism to incorporate new patterns and data types for model improvement

📊 NLP & Deep Learning Integration

Embedding Implementation: Integrated multiple word embedding techniques including GloVe and BERT/SBERT
Deep Neural Network Design: Developed specialized neural network architectures for textual pattern recognition
Feature Engineering: Created optimized feature extraction pipelines for complex text data
Cross-Library Integration: Combined tools from NLTK, Gensim, Keras, and PyTorch into cohesive solutions

🔌 API & Big Data Integration

RESTful API Development: Built scalable API layer exposing semantic type detection functionality
PySpark Integration: Created seamless connections to big data processing frameworks
Distributed Computing Support: Enabled processing of large-scale datasets through optimized architecture