Alexander Mengesha
Addis Ababa, Ethiopia
Embedded Files
Programming Languages
Programming Languages
Python
C++
NodeJS
ReactJS
ML & Gen AI Tools
ML & Gen AI Tools
PyTorch, TensorFlow
Scikit-Learn, Keras
Pandas,Airflow, Spark
SQL,chromadb, GPT
MongoDB,Hadoop
Automation Tools
Automation Tools
Docker, kubernetes
GitHub Actions
MLFlow, DVC, CML
UnitTesting
General Skill sets
General Skill sets
ML Pipeline Building
RAG Pipeline Building
EDA
Agile Integrated Development
Web-Scraping
About me
I am data scientist and ML engineer with 3+ years of work experience and a strong foundation in Python, SQL, machine learning and mathematics. Proven ability to analyze complex datasets and deliver actionable insights by developing ML Models, training and fine-tuning LLMs, building RAG systems, prompt engineering, and AI Chatbot development.
Education
ML Engineer/Data Engineer/Generative AI Engineer, Web3 Engineer
ML Engineer/Data Engineer/Generative AI Engineer, Web3 Engineer
During this intensive training, I had the opportunity to engage in ten diverse projects, each contributing to the development of the following skill sets:
Machine Learning Pipeline Development
Generative AI Pipeline Development
Data Engineering Principles and large Scale Implementations
WEB 3 dAPP Development
Statistical Modeling & Reasoning
- ALX SWE Africa ( 2022-2023 )
C ,Javascript and python
HTML, CSS, Bash Script and Linux
SQL and No SQL
NodeJ and ExpressJS
- University Of Gondar ( 2019-2022 )
Data Science and Analytics
Probability and Statistics
Machine Learning
Artificial Intelligence
Big Data & Hadoop
Deep Learning and Image Processing
University Of Gondar ( 2015-2018 )
C++, SQL , Java
JavaScript
Networking
Information Storage and Retrieval
Information Security
I instructed students in Machine Learning, and Python programming,
Guiding students through final industrial projects and a 30% increase in internships
Engaging in impactful research and community service
I supported ICT officers and created web-based Task management System
Developed a comprehensive machine learning curriculum, integrating hands-on
projects and research initiatives to enhance student engagement and understanding.
Comprehensive and Specilized Hospital (Feburary 12, 2022- Feburary 20, 2024)
Comprehensive and Specilized Hospital (Feburary 12, 2022- Feburary 20, 2024)
Developed machine learning pipeline following agile methodology, focusing on
data preparation, model building, evaluation, deployment, and communication.
Developed predictive models for healthcare challenges in Ethiopia, including:
Predicting patient length of stay in the Hospital with 98.97% accuracy using XGBoost.
Predicting childhood diarrhea incidence with 95.2% accuracy using XGBoost.
Predicting child immunization status and coverage with 90.4% accuracy using CatBoost.
Analyzed patient data, health records, and socio-economic factors to develop predictive models..
Projects
This project focuses on developing an advanced RAG-LLM application that enhances question-answering systems by integrating information retrieval with natural language generation. This innovative approach not only improves the accuracy and relevance of text interactions but also showcases the scalability and optimization of AI solutions.
This project is a cutting-edge application that leverages the power of GPT-3.5 Turbo to generate images from given text. By utilizing an OpenAI key, the project demonstrates the capability to integrate advanced AI technologies into practical applications, opening up new possibilities for content creation and visualization.
This project was done in a team with an application that optimizes the Llama-2 7B model in hugging face for the Amharic language using fine-tuning and Retrieval-Augmented Generation. It showcases the potential of fine-tuning less resource languages like Amharic with RAG for generating context-rich responses, significantly enhancing and reducing hallucination.
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