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Mulugeta Abate
Mulugeta Solomon AbateTOKYO, JAPAN

I build full‑stack systems for problems that matter.

End to end — the APIs, data models, and services (FastAPI, NestJS), the ML inside, and the cloud it runs on, with a React and Next.js front end on top. Flood‑simulation and disease‑risk platforms deployed across Africa, plus a SaaS product for the East African market.

Currently · Software Engineer, ML & AI — Sora Technology, Tokyo
Based in Tokyo, Japan · open to on-site & remote
Mulugeta Solomon Abate
01

Selected work

Production systems in climate, public health, and hospitality — the problem, my role, the stack, and what shipped.

Flood-Sight flood simulation map
Climate · Disaster response

Flood-Sight

A physics-based flood-simulation and early-warning platform, deployed for disaster preparedness in Mozambique through a JICA-linked program.

My role
ML & AWS infrastructure — backend architecture in private subnets, simulation-engine integration, and geospatial data pipelines.
Deployed · Mozambique146+ automated testsi18n · 4 languages
FastAPICeleryNext.jsMapLibre GLPostgreSQLAWSInternal platform
System architectureOne engineer · every layer

Rainfall and terrain in, a flood‑depth map out — end to end.

A Next.js front end talks to a FastAPI control plane that enqueues long‑running simulations onto Celery workers through Redis, streams live progress back over WebSocket, and tracks every job in Postgres. The flood engine — Landlab, SCS‑CN runoff, shallow‑water flow — writes GeoTIFF and PNG outputs to S3. JWT auth, transactional email, and Sentry observability round it out, deployed on Railway.

Flood-Sight system architecture: Next.js frontend, FastAPI backend, Celery workers on Redis, PostgreSQL, the flood-simulation engine, S3 storage, transactional email, and Sentry observability, deployed on Railway
Click to view full size ↗
Frontend
Next.js 16 · React 19 · TypeScript · MapLibre GL · i18n (EN/FR/PT/JA)
Backend
FastAPI · SQLAlchemy 2 async · Pydantic · WebSocket progress · JWT auth
Data
PostgreSQL 16 · Alembic migrations · Redis 7 · async pipelines
Simulation
Python · Landlab · SCS‑CN runoff · shallow‑water (SWE) · rasterio
Infra
Railway · Docker · Celery · S3 / Cloudflare R2 · Resend · Sentry
Public health · Applied ML

LSM — Larval Source Management

AI web services that detect mosquito breeding sites from drone imagery and model disease risk — a greenfield services line supporting malaria prevention across multiple countries.

My role
Architecting the detection services and inference pipeline — models, evaluation frameworks, and AWS job orchestration.
>85% precision / recall+20% accuracymAP · IoU · F1 eval
PythonPyTorchYOLOv8SQSEC2 · GPUS3Internal platform
LSM inference job pipeline sequence diagram: client, FastAPI on ECS Fargate, RDS Postgres, S3, SQS, and a GPU worker on EC2
Request flow · click to enlarge ↗
Platform architectureSORA · AWS

The infrastructure the detection services run on.

A FastAPI control plane sits in a private subnet behind API Gateway; drone imagery lands in S3, and detection jobs are decoupled through SQS so GPU workers on EC2 pull and process them independently. Job state and results persist in RDS Postgres. The same async, queue‑backed pattern lets inference scale without ever blocking the API.

SORA platform AWS architecture: clients, API Gateway, Lambda, a FastAPI service in a private subnet, SQS, EC2 GPU inference, RDS Postgres, and S3
Click to view full size ↗
Karamu restaurant dashboard
Product · SaaS · Solo build

Karamu

A restaurant reservation and management platform for the East African market — designed, built, and shipped end-to-end: product, brand, and engineering.

My role
Founder & sole engineer — product design, brand identity, full-stack build, and infrastructure.
Real-time floorM-Pesa paymentsSMS / WhatsApp
ReactNext.jsTypeScriptNodeNestJSPrismaSocket.iogetkaramu.com ↗
System architectureSolo build · every layer

A reservation system that has to be right on a Friday night.

One system, five surfaces: reservations, a restaurant dashboard, table management, QR menus, and a kitchen display system (KDS). A Next.js front end drives them against a NestJS API, with everything modeled in PostgreSQL through Prisma. Socket.io keeps the floor and the kitchen in sync in real time, M‑Pesa handles payments, and confirmations go out over SMS and WhatsApp.

Frontend
React · TypeScript · Next.js · Tailwind · real‑time floor UI
Backend
NestJS · REST · Better Auth · webhook handlers
Data
PostgreSQL · Prisma ORM · migrations
Real‑time
Socket.io · live floor + hold state
Integrations
M‑Pesa payments · SMS / WhatsApp
Infra
Vercel · Railway · Docker · Cloudflare R2 · CDN · CI/CD · getkaramu.com
02

About

Robotics gave me systems thinking. Now I point it at the whole stack.

I started in robotics — an M.Sc. at Ritsumeikan, a teaching assistantship, and a published paper on semantic line detection. Control theory, math, and sensor systems are where my instinct for infrastructure comes from.

Today I work across the whole stack: the interface people touch, the APIs and data models behind it, the ML that powers the hard parts, and the cloud that holds it up. I’ve written production SQL against real datasets, shipped real‑time features, and owned services end to end — from schema design to deploy. Startup pace taught me to go deep fast: not a little of everything, but whole systems, built to hold.

I grew up in Ethiopia and build with the East African market in mind — software for the places default tools forget. I care about systems that hold up when the stakes are real: a flood, an outbreak, a restaurant's Friday night.

03

Journey

From a bachelor's in Ethiopia to graduate robotics in Japan to shipping production ML — the path in five steps.

2016 – 2021
B.Sc. Electromechanical Engineering
Addis Ababa Science & Technology University · Ethiopia
Graduated 3.81 / 4.0. Foundations in embedded systems, control, data structures, and computer vision.
2022 – 2024
M.Sc. Robotics
Ritsumeikan University · Kyoto, Japan
MEXT Scholar. Robot control, AI, and security. Teaching assistant for 60+ graduate students, and co-authored an RSJ2024 paper on semantic line detection.
MEXT ScholarRSJ2024
2024 – 2025
A2SV — Backed by GoogleConcurrent
Competitive Programming & Software Engineering
1,500+ hours of algorithms and industry engineering practice with a program that has placed 80+ engineers at Google, Amazon, and Bloomberg.
Jul – Oct 2024
Software / ML Intern
Sora Technology · Tokyo, Japan
Optimized drone path-planning (−33% acquisition time) and migrated object detection from YOLOv5 to YOLOv8 (+22% accuracy).
Oct 2024 – Present
Software Engineer, ML & AINOW
Sora Technology · Tokyo, Japan
Architected the SORA platform backend on AWS, built Flood-Sight end-to-end, and shipped waterbody AI detection at >85% precision / recall.
AWSFlood-SightLSM
04

Stack

Languages
TypeScript · Python · Go · SQL · C++ · C
Frontend
React · Next.js · Vite · Tailwind · MapLibre GL
Backend
Node / NestJS · FastAPI · Celery · REST · WebSockets · JWT auth
Data / ML
PyTorch · TensorFlow · YOLO · OpenCV · scikit-learn · Data pipelines · Geospatial
Cloud / Infra
AWS — Lambda, API Gateway, SQS, RDS, S3, EC2 · Railway · Docker · CI/CD · PostgreSQL · Redis · Sentry
05Contact

Let's build something that matters.

Open to full‑stack and backend engineering roles where the work has real stakes — and the occasional ambitious collaboration.

Based in Tokyo, Japan · authorized to work in Japan · open to remote
Mulugeta Solomon AbateTokyo, Japan© 2026