Resume

Professional experience and qualifications

Samuel Hailemariam Seifu

Full-Stack Software Engineer — Web Applications & AI/RAG Systems

samuelhailemariam4@gmail.comAddis Ababa, EthiopiaIELTS Speaking 8.5

Summary

Full-stack engineer since 2022 building production ERP, analytics, and operational platforms end-to-end. Currently sole engineer designing a company-wide ERP at CRCC (Ethiopian branch). MSc in Artificial Intelligence; diagnosed and fixed a chunking-related retrieval failure in a RAG/analytics system through systematic debugging.

Education

Master of Science in Artificial Intelligence

Addis Ababa University

GPA: 3.94/4.0

Bachelor of Science in Software Engineering

Addis Ababa Science and Technology University (AASTU)

GPA: 3.77/4.0

Experience

Software Engineer (ERP Developer)

CRCC International Consulting (Ethiopian branch)

Jun 2026 – Present

Sole engineer building a company-wide construction ERP across 6 core modules (Project Management, Subcontract Management, HR & Payroll, Client Management, Procurement, Authentication & Authorization).

Software Engineer & Lead, Digital Services

Mizan Institute of Technology

Nov 2025 – Jun 2026

Full-stack client delivery and technical instruction in programming, data analysis, and introductory ML.

Co-Founder, Full Stack Developer & Technical Instructor

Early Start IT Solution PLC

Sep 2022 – Jul 2025

Delivered 20+ business websites and internal systems; mentored junior developers.

Software Engineer (US Remote Freelance, part-time)

YolkWorks

Jul 2024 – Feb 2025

Partnered with 5+ charity organizations on websites and software focused on UX, performance, and branding.

Full-Stack Developer

xHub Addis

Oct 2022 – Apr 2023

Built a full-stack HR management system with React, Node.js, and SQL/MongoDB.

Technical skills

AI/ML & Python

PyTorch, Python, RAG pipelines, LLM integration, Federated Learning

Full stack

React.js, Next.js, Django, Node.js, TypeScript, NestJS

Tools & platforms

Git/GitHub, Docker, FastAPI, MongoDB, PostgreSQL, AWS

Publications

SPATL-XLC: Explainability-Driven Federated Learning Framework

IEEE Access, 2025

DOI: 10.1109/ACCESS.2025.3589535