Consultant, AI Engineer

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Description

Required S. S. in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative field. Experience : 7+ years of experience in Machine Learning Engineering, with at least 1-2 years specifically focused on Computer Vision, Document AI, or Multimodal Large Language Models. Core Frameworks : Deep expertise in PyTorch and the Hugging Face ecosystem (Transformers, PEFT). Inference Engines : Hands-on, production-level experience deploying models using vLLM. Domain Expertise : Proven experience working with Document AI, Optical Character Recognition (OCR), Handwriting Recognition (HTR), or Vision-Language models. Image Processing : Proficiency in computer vision libraries (OpenCV, Pillow) and experience handling real-world, variable-quality mobile images, including tiling and chunking strategies. Responsibilities Design and optimize AI pipelines for complex document understanding. Focus on extracting structured data from mobile-captured HMIS forms, specifically tackling challenges like handwriting recognition, complex table extraction, and multilingual parsing. , Qwen-VL) and foundational OCR models on domain-specific datasets. Utilize advanced techniques (LoRA/QLoRA, DeepSpeed) to maximize accuracy on noisy, real-world mobile images. Architect and deploy production-grade inference pipelines using vLLM or similar engines. Optimize continuous batching, KV cache management, and quantization to maximize throughput while strictly maintaining our low per-page processing cost targets. Design architecture for both self-hosted/local cloud environments (like Linode) and on-premise hardware, keeping data sovereignty and cost efficiency in mind. Tune AI models for visual data optimization. Develop strategies for image chunking, tiling, and preprocessing to allow models to efficiently process high-resolution images and large, complex

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