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Consultant, AI Engineer
AI Engineer – SnapiForm Platform (Remote, Possible Travel)
PATH is a global nonprofit dedicated to achieving health equity. With over 40 years of experience in forging multisector partnerships and expertise in science, technology, economics, and advocacy, PATH develops and scales innovative solutions to the world’s most pressing health challenges.
We’re seeking an AI Engineer to help scale SnapiForm, an AI-powered platform available through Telegram mini-app, WhatsApp, and the browser. This platform enables health workers to digitize paper Health Management Information System (HMIS) forms by simply taking photos. Following a successful pilot in the Democratic Republic of the Congo (DRC), which significantly improved data accuracy and reduced reporting time, SnapiForm is now processing millions of health records monthly. In this role, you will:
Responsibilities
- Design and optimize AI pipelines for complex document understanding, with a focus on extracting structured data from mobile-captured HMIS forms, including:
- Handwriting recognition
- Complex table extraction
- Multilingual parsing
- Research, benchmark, and fine-tune state-of-the-art Vision-Language Models (VLMs, e.g., Qwen-VL) and foundational OCR models on domain-specific datasets, applying advanced techniques like LoRA/QLoRA, DeepSpeed to improve accuracy on noisy, real-world mobile images.
- Architect and deploy production-grade inference pipelines using vLLM or similar engines, optimizing:
- Continuous batching
- KV cache management
- Quantization to maximize throughput while maintaining low per-page processing costs.
- Design scalable infrastructure for:
- Self-hosted/local cloud environments (e.g., Linode)
- On-premise hardware, ensuring data sovereignty and cost efficiency.
- Tune AI models for visual data optimization, including strategies for:
- Image chunking
- Tiling
- Preprocessing to efficiently process high-resolution images and large, complex tables without losing data context.
- Evaluate, select, and provision optimal cloud and on-premise GPU infrastructure to handle 10 million forms/month.
- Assess next-gen hardware (e.g., NVIDIA Blackwell nodes) to balance scalability, performance, and budget efficiency.
- Lay technical foundation for future capabilities, including:
- Offline/edge processing
- Expanded multilingual support
- Interoperability beyond DHIS2.
- Travel willingness: Can accommodate travel to PATH countries as needed while overlapping with GMT and ESA time zones.
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Requirements & Qualifications
Education
- B.S. or M.S. in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative field.
Experience
- 7+ years in Machine Learning Engineering, with at least 1–2 years focused specifically on:
- Computer Vision
- Document AI
- Multimodal Large Language Models
Core Frameworks & Tools
- Deep expertise in PyTorch and the Hugging Face ecosystem (Transformers, PEFT).
- Production-level experience deploying models using vLLM.
- Proven experience in:
- Document AI
- Optical Character Recognition (OCR)
- Handwriting Recognition (HTR)
- Vision-Language Models
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Technical Skills
- Proficiency in OCR and mobile image handling, including:
- OpenCV
- Pillow (PIL)
- Tiling and chunking strategies for real-world, variable-quality images.
- Strong knowledge of:
- Docker and Kubernetes
- Distributed training/inference optimization
- Cloud GPU provisioning
- Exceptional Python development skills for writing:
- Clean, modular code
- Highly optimized solutions
Language
- Fluent verbal and written English.
Personal Attributes
- Passion for building technology that improves health systems and supports frontline workers in low-resource settings.
- Focus on cost-effective, scalable AI solutions optimized for limited hardware.
- Ability to bridge cutting-edge AI research with practical engineering to meet real-world constraints.
- Proactive, independent-working yet collaborative mindset.
- Strong sense of accountability and commitment to continuous improvement.
What We Offer
- Opportunity to contribute to impactful digital health and data initiatives.
- Competitive compensation and flexible working arrangements.
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