Shaun Mendes

With 6+ years of experience in ArtificaI Intelligence and Machine Learning, I specialize in designing scalable, data-driven solutions that deliver measurable business impact. My expertise spans Generative AI, Advanced Natural Language Processing(NLP), Recommendation Systems, Speech/Audio Processing and Computer Vision(CV), with a strong foundation in building scalable MLOps/LLMOps pipelines.

Prior to joining Stevens, I had been working as a Senior Data Scientist at HERE Technologies and Fractal Analytics building and deploying machine learning, deep learning, generative AI, natural language processing and computer vision models.

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Professional Experience
Senior Data Scientist | HERE Technologies
April 2021 - August 2023

Worked with Place Creation team at HERE Technologies to expand the coverage of HERE Maps by 17% across 9 countires by extracting geospatial data from websites using state of the art machine learning, deep learning and Large Language Models generating over 10 million high-quality records. Crafted a scalable MLOps/LLMOps pipeline on AWS, leveraging optimized CPU/GPU instance selection and model compression techniques, including Knowledge Distillation and ONNX.

Tools and Technologies : pytorch, tensorflow, pytorch lightning, langchain, LLMs, scikit-learn, pandas, numpy, Fast API, react, ONNX, AWS, Nvidia DGX A100 Cluster, MLFlow, Docker, Git, CI/CD
Machine Learning Engineer | Fractal, Inc.
August 2017 - April 2021

Worked with the AI@Scale team at Fractal on building the platform solutions in machine learning, deep learning and big data. Worked with Fortune 500 clients such as Colgate , Autodesk and Procter & Gamble on projects involving forecasting, predictive analytics, big data technologies, speech and natual language processing using machine learning.

Tools and Technologies : python, pytorch, tensorflow, pytorch lightning, docker, git, Fast API, django, Microsoft Azure, AWS, Airflow, Jenkins, Numpy, Pandas, Scipy, Scikit-learn, Statsmodel, Matplotlib, Jupyter, langchain, sentence transformers, huggingface

Internship Experience
Data Scientist | HERE Technologies
May 2024 - August 2024

Worked with the Place Creation team to accelerate data preparation, training, and testing for multiple machine learning and deep learning models by 60% through streamlining routine processes and leveraging Large Language Models (LLMs) such as Llama3 and OpenChat with carefully crafted prompts for feature extraction and model evaluation. I also built an AI Chatbot to assist with onboarding and information retreival.

Tools and Technologies : python, pytorch, pytorch lightning, langchain, sentence transformers, huggingface
Open Source Projects
Controllable subject guided text-to-image generation and editing using diffusion models

Developed pipeline for personalized text-to-image generation and editing using latent diffusion models (generative AI), incorporating LoRA finetuning for image generation and cross-attention guidance for image editing (pytorch / hugging face diffusers /stable diffusion)

Tools and Technologies : python, pytorch, hugging face diffusers, stable diffusion, OpenCV, transformers
Stevens Institute of Technology Chatbot
Code

Designed Large Language Model chatbot to assist students, faculty, and prospective applicants by providing detailed information about various aspects of university life and administration at Stevens Institute of Technology. Built using Langchain, Streamlit, and GPT-4, this chatbot offers a user-friendly interface and comprehensive details across a range of topics.

Tools and Technologies : python, pytorch, hugging face, Streamlit, Ollama, Langchain, transformers
Hybrid Recommendation Engine using LLama2
Code

Fine-tuned Llama2 chatbot using QLoRA, tailored to provide detailed information and recommendations about movies. The model is fine-tuned on the IMDB dataset, enabling it to generate informed and contextually relevant responses.

Tools and Technologies : python, pytorch, huggingface, transformers
Instacark Market Recommendation System
Code

Modeled a collaborative filtering-based recommender systems for personalized Instacart recommendations, comparing performance with TF-IDF, Singular Value Decomposition(SVD), and Bayesian Personalized Ranking(BPR) methods

Tools and Technologies : python, pandas, numpy, matplotlib, lightFM, implicit
Brewery Sales Forecasting
Code

Project involves forecasting total sales for a brewery using a dataset from Kaggle. The models used for forecasting include Linear Regression, Random Forest, and Decision Tree on PySpark. The project has been executed and tested on Google Cloud Platform's DataProc service.

Tools and Technologies : python, numpy, matplotlib, pyspark, matplotlib, Google Cloud Platform
DineMate: Dinout Assistant using GPT-4
Code

This project is a restaurant chatbot designed using Langchain and GPT, with a frontend built in Streamlit and APIs managed by FastAPI. The chatbot interacts with customers to provide personalized food recommendations and resolve customer complaints. All data is stored in a SQLite database.

Tools and Technologies : python, pytorch, hugging face, Streamlit, Ollama, Langchain

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