Senior Deep Learning Engineer

Nanonets is revolutionizing the way companies streamline document-heavy and unstructured data workflows through AI Agents. Our client portfolio includes industry giants such as Adobe, Schneider Electric, and Boston Scientific. With strong support from renowned investors, we are experiencing rapid growth and are seeking exceptional engineers to join our purpose-driven team. As a part of our team, you will play a crucial role in developing and implementing cutting-edge generalised deep learning architectures that are capable of addressing intricate business challenges. Your primary focus will involve creating solutions for converting unstructured data into a structured format without the need for manual feature or model adjustments. We expect you to design state-of-the-art models that are globally recognized for their effectiveness in solving these complex problems. Additionally, you will be responsible for continuously exploring and integrating the latest advancements in the field to enhance these architectures. Ideal candidates should possess: - 5-8 years of hands-on experience in Deep Learning - Solid foundational understanding of deep learning concepts and architectures, including LLMs and VLMs - Demonstrated expertise in at least one specialized area of deep learning such as NLP, computer vision, or multimodal models - Proven track record of building and implementing production-grade Deep Learning systems at scale - Familiarity with various large language models like GPT, LLaMA, and Claude, along with their practical applications - Strong software engineering skills encompassing version control, CI/CD practices, and code quality assurance - Ability to quickly adapt and utilize new technologies and methodologies effectively Noteworthy projects completed by our Senior DL Engineers: - Deployment of large-scale multi-modal architectures proficient in comprehending both text and images with high accuracy - Development of an auto-ML platform capable of autonomously selecting the optimal architecture and fine-tuning method based on data type and volume - Creation of world-class models for processing diverse documents like invoices, receipts, passports, driving licenses, etc. - Implementation of robust modelling techniques for extracting hierarchical information from documents with tree-like structures - Extraction of complex tables with intricate formats such as wrapped tables, multi-field columns, cells spanning multiple columns, and tables in distorted images - Facilitation of few-shot learning through cutting-edge finetuning techniques that are at the forefront of the industry Join us at Nanonets and be a part of a dynamic team that is shaping the future of AI-driven document automation solutions.,