AI / ML
Engineer

What I do?

• Machine Learning

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  Supervised Learning – expertise in regression, classification, and ensemble methods using scikit-learn, XGBoost, and LightGBM.
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  Unsupervised Learning – Clustering, dimensionality reduction, and anomaly detection for pattern discovery in complex datasets.
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  Reinforcement Learning – Building agents that learn optimal policies through interaction with environments using PyTorch and TensorFlow.
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  Feature Engineering – Creating meaningful features from raw data to improve model performance and interpretability.
• Deep Learning

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  Neural Networks – Designing and training custom neural network architectures for various applications using PyTorch and TensorFlow.
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  Transfer Learning – Leveraging pre-trained models to achieve high performance with limited data across domains.
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  GANs – Implementing Generative Adversarial Networks for image generation and data augmentation.
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  Model Optimization – Techniques for model compression, quantization, and deployment on edge devices.
• NLP

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  Transformer Models – Fine-tuning and deploying BERT, GPT, and other transformer-based models for various NLP tasks.
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  Text Classification – Sentiment analysis, topic modeling, and document classification using state-of-the-art techniques.
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  Named Entity Recognition – Extracting structured information from unstructured text using custom NER models.
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  Question Answering – Building systems that can understand and respond to natural language questions with contextual awareness.
• Computer Vision

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  Object Detection – Implementing YOLO, Faster R-CNN, and other architectures for real-time object detection.
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  Image Segmentation – Pixel-level classification for medical imaging, autonomous driving, and industrial applications.
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  Face Recognition – Building secure and accurate face recognition systems with anti-spoofing capabilities.
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  Video Analysis – Action recognition, tracking, and anomaly detection in video streams using deep learning.