Full-stack · AI integration · Remote-first
I build end-to-end web products with clean APIs, reliable services, polished interfaces, and performance and maintainability baked in. AI tools extend my throughput; engineering judgment sets the bar.
Introduction
Chala Megersa Damesa
Software engineer
I own features across the stack: data models, services, APIs, and client apps, with a bias for performance, clarity, and UX that survives real usage.
AI-assisted workflows are central to how I operate day to day: Cursor and Copilot keep repetitive work lightweight so attention stays on architecture, testing, and product tradeoffs. I also ship applied AI work where it fits: retrieval-augmented generation (RAG), LangChain, and LLM integrations, alongside classic full-stack delivery.
I'm focused on growing as a full-stack engineer while staying aligned with what teams expect from modern stacks: speed without sacrificing maintainability.
Stack
Timeline
Selected work
Applied AI · RAG product
AI-assisted guidance for Ethiopian entrepreneurs using RAG and LangChain, with localized startup context and curated answers that reduce noisy, generic advice.
End-to-end P2P vehicle rental platform with chat, notifications, verification, subscriptions, and admin, in a focused build using Cursor and AI-assisted development.
Brazil-focused Bingo 90 experience with live rounds, realtime results, and interaction patterns tuned for sustained play sessions.
Control center for pantry inventory and meal workflows with focused admin UX for monitoring usage and keeping day-to-day operations smooth.
Israel-focused Bingo 90 platform with realtime gameplay and resilient WebSocket connectivity for interactive sessions.
Skills assessment and visualization experience built on Chart.js with interactive charts for professionals who want clarity, not clutter.
Process
AI is leverage for clarity and speed, not a substitute for judgment. Here is how I use it alongside solid engineering practice to ship work that holds up after launch.
Cursor, Copilot, and RAG-style assistants accelerate typing and scaffolding. I stay in the loop on architecture, tradeoffs, and edge cases so output stays production-shaped.
Small vertical slices, fast feedback, and repeatable prompts keep delivery predictable. Velocity shows up as shorter cycles, not skipped review or testing.
Less time on repetitive boilerplate means more room for domain modeling, API contracts, UX details, and the reliability work users actually feel.
The goal is maintainable systems: clear boundaries, sensible abstractions, and pragmatic test coverage, especially when timelines are aggressive.
P2P car and motorbike rental with booking, admin, and core rental flows, delivered as a working MVP on a compressed timeline using Cursor and the same AI-assisted workflow I use on client work. A scope like this is often planned in weeks or months; here the focus was disciplined execution end to end.
Visit addiswheels.comStudies
Addis Ababa University
Graduated 2021
Next step
Full-stack delivery, pragmatic AI leverage, and clear communication, whether you need a production feature, an MVP, or an integration path that won't crumble under real traffic.
Top-rated on Upwork · Strong client feedback history
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