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Job Title: Sr. AI Engineer About the Role: We are looking for a skilled Sr. AI Engineer / Data Scientist with hands-on experience in OCR pipelines and Large Language Model (LLM) fine-tuning. The ideal candidate will work on developing, fine-tuning, and optimizing a vision-language model (VLM) to extract structured information from scanned documents. You will collaborate with data scientists and backend engineers to build an accurate, high-throughput, and production-ready AI pipeline. Key Responsibilities: · Fine-tune and adapt multimodal LLMs (e.g., Qwen-VL, LLaVA, or similar) for domain-specific document understanding. · Design prompt templates and instruction sets to improve JSON-structured output quality. · Perform different fine-tuning and incremental learning across multiple datasets to ensure generalization. · Implement evaluation metrics and validation datasets to track model accuracy and performance. · Optimize inference performance using quantization, LoRA adapters, and Ray Serve / vLLM / Unsloth frameworks. · Collaborate with backend teams to integrate the model into production pipelines. · Build monitoring tools to log model confidence, token usage, and inference latency Required Skills & Experience: - Strong Python programming skills with experience in PyTorch and Transformers (Hugging Face). - Experience working with OCR tools such as PaddleOCR, Tesseract, or EasyOCR. - Hands-on experience fine-tuning or serving LLMs / VLMs (e.g., Qwen, LLaVA, Mistral, or Vicuna). - Knowledge of LoRA / QLoRA / PEFT adapters for efficient model training. - Familiarity with JSON schema generation, prompt engineering, and structured data extraction. - Experience using Unsloth, Ray Serve, or vLLM for large-scale inference. - Comfortable working with Docker, CUDA, and NVIDIA GPUs for model deployment. - Strong understanding of tokenization, attention mechanisms, and quantization (4-bit / 8-bit ) Nice to Have: - Experience with the LLM model serving - Exposure to CI/CD pipelines and Azure / AWS / GCP deployments. - Familiarity with PDF parsing frameworks (e.g., Camelot, PyMuPDF). - Prior work on document intelligence or AI-based invoice understanding systems. What We Offer: · Opportunity to work with cutting-edge Vision-Language AI models. · Hands-on involvement in production-grade OCR-LLM pipelines. · Competitive compensation and flexible work culture. · Collaborative environment focused on AI innovation in enterprise document automation