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NanoNets is building AI agents for complex business processes, specializing in understanding unstructured data and applying business rules across accounts payable, order management, and supply chain workflows. The company's agents handle exceptions that other tools miss, reducing processing time by 94% while delivering clean data to enterprise systems like SAP and Salesforce.
As a Deep Learning Engineer, you will design and deploy cutting-edge generalized deep learning architectures that solve complex business problems—converting unstructured data into structured formats without manual feature engineering. You'll build state-of-the-art models that advance the field, continuously experimenting with and incorporating new research into production systems.
You bring 5–8 years of deep learning experience with strong foundational knowledge of modern architectures including LLMs and vision-language models (VLMs). You have demonstrated expertise in at least one specialized area—NLP, computer vision, multimodal models, or similar—and a track record of building and deploying production-grade deep learning systems at scale. You're familiar with major language models (GPT, LLaMA, Claude) and their applications, and you practice strong software engineering discipline (version control, CI/CD, code quality).
Recent projects completed by senior engineers on the team include deploying large-scale multimodal architectures for joint text-image understanding, building auto-ML platforms that select optimal architectures and fine-tuning methods based on data characteristics, creating world-class models for document processing (invoices, receipts, passports, licenses), implementing hierarchical information extraction for nested document structures, handling complex table extraction from warped and multi-column layouts, and enabling few-shot learning through state-of-the-art fine-tuning techniques.
This is an opportunity to work on high-impact AI infrastructure at a Series B company solving real enterprise problems with deep learning.
