AI Engineer

We are seeking an AI Engineer to join our team and drive innovation in Document AI. In this role, you will work with enormous volumes of document data spanning structured, semi-structured, and unstructured formats. Your focus will be on leveraging state-of-the-art techniques from machine learning, computer vision, and natural language processing to build robust pipelines for document understanding and information extraction.

A key responsibility will be transforming raw documents into LLM-ready data that can be seamlessly consumed by our AI agents, enabling automation, reasoning, and advanced decision-making. You will collaborate with crossfunctional teams to design scalable solutions that push the boundaries of Document AI research and application.

Responsibilities

◦ Design and develop AI/ML models and pipelines for document classification, extraction, and understanding across structured and unstructured formats.
◦ Build and maintain end-to-end pipelines that integrate ML models into scalable workflows for document ingestion, preprocessing, inference, and output generation.
◦ Continuously improve and optimize pipelines for scalability, robustness, efficiency, and production-readiness.
◦ Fine-tune computer vision models (e.g., CNN, Transformer-based architectures) for tasks such as layout analysis, object detection, and information extraction.
◦ Conduct research and experimentation to improve the accuracy, robustness, and adaptability of Document AI systems.
◦ Stay updated with state-of-the-art advancements in machine learning, NLP, and computer vision, and apply them to real-world document processing challenges.

Your skills and experience

◦ 2+ years of experience in advanced Machine Learning, with a strong preference for expertise in Computer Vision and Image Processing.
◦ Strong proficiency in Python and ML frameworks such as PyTorch (TensorFlow), Hugging Face, Scikit-learn, OpenCV, and ONNX.
◦ Experience in deploying machine learning models into production environments, ensuring scalability, reliability, and efficient inference.
◦ Solid understanding of modern deep learning architectures such as CNNs, Transformers, ViT and other related models.
◦ Experience in training, fine-tuning, and evaluating models for large-scale document/image data.
◦ Strong problem-solving and analytical skills with the ability to design innovative solutions.
◦ Effective communication and collaboration skills.

◦ Experience with OCR technologies (e.g., Tesseract, PaddleOCR, EasyOCR) or VLM-based OCR approaches for robust document text extraction.
◦ Hands-on experience with real-time object detection models (e.g., YOLO, RTDETR, D-FINE) and their application in document layout understanding.
◦ Familiarity with document extraction frameworks such as Docling, MinerU, or similar.
◦ Experience with LLMs and embeddings to make document data AI agent–ready.
◦ Familiarity with MLOps practices (CI/CD, containerization, deployment, monitoring).