Deep Learning Engineer
About Us:
Nanonets agents are built for complex business processes. Ranked #1 in understanding unstructured data and applying business rules in processes like accounts payable, order management, and supply chain.
Nanonets agents handle the exceptions other tools miss, reducing processing time by 94% and delivering clean data to SAP, Salesforce, or any system of record. That's why global enterprises reach for Nanonets when workflows are complex and accuracy is non-negotiable.
Learn more about us here:
About the Role
The role can be summed up as building and deploying cutting edge generalised deep learning architectures that can solve complex business problems like converting unstructured data into structured format without hand-tuning features/models. You are expected to build state of the art models that are best in the world for solving these problems, continuously experimenting and incorporating new advancements in the field into these architectures.
What we’re looking for
5-8 years of experience in Deep Learning.
Strong foundational knowledge in deep learning concepts and architectures (LLMs and VLMs)
Demonstrated expertise in at least one specialised area of deep learning (NLP, computer vision, multimodal models, etc.)
Experience building and deploying production-grade Deep Learning systems at scale,
Familiarity with various large language models (GPT, LLaMA, Claude, etc.) and their applications
Strong software engineering practices including version control, CI/CD, and code quality
Ability to rapidly learn and apply new technologies and approaches.
Interesting Projects Other Senior DL Engineers Have Completed
Deployed large scale multi-modal architectures that can understand both text and images really well.
Built an auto-ML platform that can automatically select the best architecture, fine-tuning method based on type and amount of data.
Best in the world models to process documents like invoices, receipts, passports, driving licenses, etc.
Hierarchical information extraction from documents. Robust modeling for the tree-like structure of sections inside sections in documents.
Extracting complex tables - wrapped around tables, multiple fields in a single column, cells spanning multiple columns, tables in warped images, etc.
Enabling few-shots learning by SOTA finetuning techniques.