Embedded Files
Henock Kinfegabrail Dessalegn
Addis Ababa, Ethiopia
Addis Ababa University (2016-2021)
BSc. (Information Technology)
BSc. (Information Technology)
Email: henockgebriel@gmail.com
linkedin.com/in/henock-kinfegabrail-dessalegn-5362b6202
Software Engineering
Software Engineering
Python
CI/CD
Git
SQL CML
Gen AI
Gen AI
Prompt design
Pandas
Git
Tokenization
Embedding
RAG
Fine-tuning
Data Engineering
Data Engineering
MLflow
Docker
ETL
Web Development
Web Development
HTML5
CSS Stream lit
JavaScript
About me
A junior Generative AI engineer with experience building generative AI models, developing RAG systems, finetuning LLMS, and Prompt engineering. With a background in web design and development. Demonstrated experience in ETL pipeline development, data processing, and Docerization.
Education
10 Academy (2024)
- Generative AI, Machine Learning, and Data Engineering
Addis Ababa University (2016-2021)
- BSc. (Information Technology)
Work Experience
- Tiyake – Addis Ababa, Ethiopia— Web developer
April 2023 - July 2023
Designed and developed a questionnaire website tailored for students, resulting in an engaging user experience, by collaborating closely with a team to integrate interactive features and validation mechanisms.
- Alta Computech, Addis Ababa, Ethiopia— Web developer
May 2022 - February 2023
May 2022 - February 2023
Developed and maintained the company's internal communication website, ensuring the site was accessible and functional across various devices and screen sizes, by implementing responsive design principles.
Projects
RAG Q&A— Legal document assistant
RAG Q&A— Legal document assistant
Developed and implemented a Q&A pipeline using Langchain, ensuring strong performance through thorough testing and delivering detailed performance reports, by integrating components, designing and executing evaluation pipelines with RAGAS, and analyzing system performance.
AI-driven Amharic Text Generation
AI-driven Amharic Text Generation
AI-driven Amharic Text Generation
Developed an Amharic Retrieval-Augmented Generation (RAG) pipeline, creating content based on campaign details and Telegram channel history, by using LLaMa 2 models and ChromaDB
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