Engen Sherpa

London, England, United Kingdom

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Summary

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Rockstar

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Top School

Engen Sherpa is an AI Engineer based in London with nine years of experience building and productionising ML systems across startups and enterprise hospitality. He has delivered end-to-end solutions from mobile-ready computer vision and image captioning to ETA forecasting and RAG-powered knowledge retrieval, driving measurable business impact such as halving inventory costs. At Utopian AI he integrates LLMs, automation and CI/CD pipelines to operationalise customer service agents and internal search for hospitality teams. His background spans founding a messaging startup, contributing to deep learning coursework implementations on GitHub, and optimizing on-device models—evidence of both research-mindedness and product-focused delivery. Engen pairs an MSc in Artificial Intelligence with hands-on engineering, favouring pragmatic, test-driven approaches to AI safety and applied ML.

9 years of coding experience

Bachelor of Science (BSc) Computer Science and Business Information Technology, Bachelor of Science (BSc) Computer Science and Business Information Technology at Prifysgol Cymru Y Drindod Dewi Sant / University of Wales Trinity Saint David

Foundation degree Computing/Information Technology/Web Development, Foundation degree Computing/Information Technology/Web Development at CTL

Machine Learning Engineer Nanodegree Computer Science, Machine Learning Engineer Nanodegree Computer Science at Udacity

Msc Artificial Intelligence, Msc Artificial Intelligence at University of Surrey

English

Github Skills (6)

neural-network10

machine-learning10

logistic-regression10

deep-learning10

python10

numpy10

Programming languages (3)

C++Jupyter NotebookPython

Github contributions (5)

enggen/Deep-Learning-Coursera

Oct 2017 - Nov 2018

Deep Learning Specialization by Andrew Ng, deeplearning.ai.

Role in this project:

Data Scientist

Contributions:196 commits, 189 pushes, 1 branch in 1 year 1 month

Contributions summary:Engen contributed to the development of a deep learning model, specifically a logistic regression classifier implemented with a neural network mindset. This included initializations of model parameters, computations of the cost function and its gradient, and the application of an optimization algorithm (gradient descent). The user also organized these elements into a cohesive model function.

deeplearning-aispecializationandrew-ngdeep-learningmachine-learning

enggen/YouTube-iOS-App

Apr 2017 - Feb 2018

Contributions:35 commits, 34 pushes, 1 branch in 10 months

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