Overview

Machine Learning Engineer (Document Intelligence & OCR)

Responsibilities

• Design, train, and deploy Machine Learning and Deep Learning models.
• Develop Document Intelligence and OCR solutions for paystubs, bank statements, invoices, tax forms, and identity documents.
• Build and optimize CNN and Vision Transformer (ViT) models for document classification, layout analysis, object detection, and text extraction.
• Develop scalable AI services using Python, FastAPI, or Flask.
• Improve model accuracy, inference performance, and production scalability.
• Collaborate with engineering teams to deploy AI solutions in cloud environments.

Qualifications

• Impact at VELLOE
• As a Machine Learning Engineer (Document Intelligence & OCR), you will build AI-powered OCR and intelligent document processing solutions using Computer Vision, Deep Learning, and Document Intelligence.
• You will have hands-on experience in designing, training, and deploying CNN and Vision Transformer (ViT) models for document classification, information extraction, fraud detection, and OCR of financial and identity documents.
• Programming: Python, FastAPI, Flask.
• Machine Learning: Scikit-learn, XGBoost.
• Deep Learning: PyTorch, TensorFlow, Keras.
• Computer Vision: CNN, Vision Transformers (ViT), OpenCV, YOLO.
• Document Intelligence: OCR, Intelligent Document Processing (IDP), Layout Analysis, Key-Value Extraction.
• Cloud & DevOps: Docker, Git, AWS/Azure.
• Databases: PostgreSQL, MongoDB, Redis.
• Generative AI: Hugging Face, LLMs, LangChain, RAG.
• Hands-on experience designing, training, and deploying CNN and Vision Transformer (ViT) models.
• Comfortable working across document classification, information extraction, fraud detection, and OCR of financial and identity documents.
• Machine Learning, Deep Learning, Python, Computer Vision, CNN, Vision Transformer (ViT), OCR, Document Intelligence, Intelligent Document Processing (IDP), PyTorch, TensorFlow, OpenCV, YOLO, FastAPI, Flask, Scikit-learn, Hugging Face, LLM, LangChain, RAG, Docker, PostgreSQL, MongoDB, Redis. .