Deep Learning Engineer

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 a 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.

Requirements:

• 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 multimodal architectures that can understand both text and images really well. Built an auto-ML platform that can automatically select the best architecture and 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 modelling 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-shot learning by SOTA fine-tuning techniques.
• Hands-on model training: Candidates must have actually trained or fine-tuned DL models. Look for: pre-training, continued pre-training, SFT, PEFT/LoRA/QLoRA, distillation, RLHF/RLAIF, SSL, domain adaptation.
• Strong preference: Modern Transformer / Foundation Models.
• Look for hands-on work with: LLaMA / Qwen / Mistral / Gemma / GPT-style models; Vision-Language Models: Qwen-VL, LLaVA, etc. Vision Transformers / ViT LayoutLM / document Transformers Speech/Audio Transformers Custom Transformer architecture development.