AI Engineer / Senior AI Engineer ? Document Intelligence

Job Description

We are looking for someone obsessed with turning messy real-world documents into perfectly structured, actionable data. If you live and breathe document layout analysis, OCR post-processing, and visual + language models, this role is custom-built for you. Core Responsibilities Design and build production-grade Document Intelligence pipelines (invoices, contracts, forms, reports, handwritten, tables, multi-language, etc.) Train/fine-tune and deploy Layout-aware models (LayoutLMv3, Donut, LLaMA-Adapter, Nougat, etc.) Build and optimize Vision-Language models (VLLMs) on custom enterprise document datasets Improve OCR accuracy using layout context, post-correction with LLMs, and geometric reasoning Own the full stack: data preparation ? model training (PyTorch) ? evaluation ? ONNX/TensorRT optimization ? FastAPI deployment Push boundaries on table extraction, key-value pairing, nested hierarchies, and multi-page document understanding Must-Have Skills & Experience Very strong understanding of document layout analysis (bounding boxes, reading order, logical blocks, nested tables, headers/footers, multi-column detection) Hands-on experience with modern Document AI architectures: LayoutLMv1/v2/v3, DocFormer, LayoutXLM, Donut, Pix2Struct, Nougat, UDOP, etc. Vision-Language models (LLaVA, Qwen-VL, InternVL, PaliGemma, etc.) Deep experience fine-tuning and serving LLMs & VLLMs (Llama-3, Mistral, Phi-3-vision, Qwen, etc.) using PEFT (LoRA/QLoRA), vLLM, TGI, or Ollama Strong PyTorch proficiency (custom trainers, distributed training with DDP/FSDP, TorchCompile, mixed precision) Solid grasp of OCR ecosystems and post-processing (Tesseract, EasyOCR, PaddleOCR, AWS Textract/Google Document AI limitations and how to beat them) Experience building datasets from real enterprise documents (Labelling tools: UBIAI, Label Studio, Doccano, custom UI) Good applied math: Transformers, attention mechanisms, positional encodings (especially 2D layouts), RoPE, ALiBi Nice-to-Have (Big Bonus) Published research or open-source contributions in Document AI / VLLM space Experience with multimodal RAG over documents ONNX / TensorRT / DeepSpeed optimization for low-latency inference Kubernetes + GPU scheduling (we run our own bare-metal cluster) Who thrives here? You get excited when you see a 50-page scanned purchase order with overlapping stamps and handwritten notes ? because you already know exactly how you?re going to destroy it. Perks Work directly on enterprise deals worth crores ? your model = real revenue impact Unlimited GPU access (A100s & H100s in-house)