Utshav Paudel · Clinical & Healthcare AI Engineer

Experience

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current

Jun 2025 → Present

MedForce AI London, UK · Remote

AI engineer on a clinical-automation platform deployed in an NHS pilot. Designed the agent architecture and clinical RAG pipeline targeting clinician documentation and decision-support workflows.
Fine-tune domain-specific LLMs (Mistral 8×22B, Llama 3, Command R family) on curated hepatology cases for medical reasoning and structured documentation; build clinical evaluation harnesses covering factuality, citation grounding, and hallucination rate.
Cut inference cost and latency for real-time clinical use through various caching techniques (KV cache, prompt caching, semantic response cache), request batching, and quantization; built monitoring for latency, drift, and hallucination signals.

Feb 2024 → Jun 2025

Digital-Dandelion London, UK · Remote

Built a clinical RAG system over EASL liver-disease guidelines using a multi-route retrieval architecture: simple queries served by embedding-based retrieval, complex queries routed to page-indexed advanced RAG. Reached 97% answer accuracy vs. 85% on a state-of-the-art baseline and cut end-to-end latency by 50%.
Trained an EfficientNet image-classification system on GCP scoring 30,000+ dental websites for modernization; lifted accuracy from 89% → 95% via targeted data augmentation and an architecture switch from the prior baseline. Released the dental scraping and extraction datasets on Hugging Face.
Built a multimodal ranking system for JLL evaluating hundreds of commercial real-estate offices via pairwise tournament comparisons across building exterior, interior, workspace, and floor-plan imagery to identify top performers.
Fine-tuned an LLM on hundreds of past winning creative campaigns and creative-director rubrics for Page & Page (Novo Nordisk account); validated outputs on KPIs spanning relevance, creativity, and brand-fit.

Jul 2023 → Sep 2023

Omdena New York, USA · Remote

Coordinated a 10–20-person distributed team across multiple timezones building scraping infrastructure that assembled a 46,000-article Nepali news corpus, the working dataset for the program's media-representation research.
Built Nepali-language NLP models capturing local idioms to classify how women and marginalized groups are represented in Nepali news media.
Designed a media-diversity scoring model with explicit progress metrics surfaced to research stakeholders.