AI Engineer / Senior AI Engineer – Document Intelligence

If you live and breathe document layout analysis, OCR post-processing, and visual + language models, this role is custom-built for you.

• 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

• 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

• 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)

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.

• Work directly on enterprise deals worth crores — your model = real revenue impact
• Unlimited GPU access (A100s & H100s in-house)