Humayun I.

Senior ML Engineer. Specializing in; Computer Vision | Medical Image Analysis | LLMs | NLP | Generative AI | Chatbot Development | Data Extraction, NER.

Humayun Irshad is a Machine Learning and Computer Vision Leader, specialized in deep learning and active learning with domain expertise and a proven track record of designing, delivering and supporting innovative AI applications across multiple domains serving customers around the world.

He is experienced in delivering Computer Vision and Data Science products and APIs for use cases including image recognition and classification, object detection and segmentation in 2D and 3D images/videos - application ranging from climate, satellite, medical, facial identity, self-driving vehicles, retail and fashion. With over a decade of experience in Machine Learning and Computer Vision practices, applying and training deep learning algorithms (CNN, RCNN, LSTM, Transformers), image processing methods, feature extraction techniques, pattern recognition, feature selection/regularization, boosting methodologies, generalized regression models, data visualization, analytic approximation, enumerative and graphical combinatorics, advanced implementations of operator algebras, probability theory, and statistics.

Awards

🥇 Susan G. Komen Postdoctoral Fellowship 2017
🥇 Winner of ISBI Challenge on Breast Cancer Detection (Camelyon 2016)
🥇 Runner Up of ICPR MITOS Contest 2012
🥇 Winner of World Summit Youth 2009
🥇 Superwisdom Certified

Core Skills & Expertise

✅ Computer Vision Applications
✅ Regulatory-Approved Medical AI
✅ LLM & Generative AI Proficiency
✅ Prompt Engineering & Model Fine-Tuning
✅ Production-Grade AI Deployment
✅ Speech Recognition & Audio Processing
✅ Generative AI Across Modalities
✅ RAG Chatbots & Intelligent Retrieval
✅ Cloud Platforms & MLOps Workflows

Project Experience Highlights:

Robotics Process Automation for Software Development (RPAD):

• Developed and led the creation of a robust AI/ML infrastructure that drives the logic behind Record/Replay and Accessibility testing.
• This infrastructure involves capturing AI bot interactions with various devices, storing and processing this data for AI models, and integrating a Data Labeling system.
• It includes a dedicated model training environment using SageMaker and a real-time model serving system. Contributed to product development in the field of Robotics Process Automation for Software Development (RPAD).