Backend Systems
APIs, data models, reliability
Engineering lab / Product systems
Full-stack products with backend depth, interface care, and delivery discipline.
I am Sylvain Kadjo, a software engineer building useful systems with Python, React, TypeScript, cloud infrastructure, automation, and AI/data workflows.
Frontend Products
React, UX states, design systems
Cloud & Deployment
Edge-aware delivery, CI/CD
AI/Data Workflows
Signals, review, interpretation
Automation
Operational handoffs, documents
Product UX
Flows, clarity, maintainability
Case studies that show product judgment, not just implementation.
Each project is framed as a system: problem, role, stack, architecture, and what it proves.
Education search and comparison platform
Edumatch
A product concept for helping families compare schools through structured information, search behavior, and fit signals.
- Role
- Product engineer across UX structure, data modeling, interface direction, and implementation planning.
- Next.js
- TypeScript
- Python
- PostgreSQL
- Search UX
Personal brand platform with blog and project content
Portfolio & Publishing System
A Next.js portfolio/blog system that combines public pages, Supabase-backed content, rich text editing, and Cloudflare deployment.
- Next.js
- React
- TypeScript
Machine learning workflow for suspicious activity signals
Fraud Detection ML Project
An applied ML project focused on identifying suspicious transaction patterns and turning model output into reviewable product signals.
- Python
- Pandas
- Scikit-learn
Workflow automation for document generation and operational handoffs
Salesforce Document Automation
A systems project around automating document generation and reducing manual steps in Salesforce-adjacent workflows.
- Salesforce
- Automation
- Document generation
What I can own on a modern software team.
The strongest signal is not that I know tools. It is where I can take responsibility in a product system.
Frontend Engineering
Product interfaces that stay fast, accessible, and maintainable.
- React
- Next.js
- TypeScript
- Tailwind CSS
- UI systems
Backend Engineering
APIs, services, and data models designed for real product flows.
- Python
- FastAPI
- Django
- SQLAlchemy
- PostgreSQL
Data & AI
Practical ML and automation work that turns data into decisions.
- ML experiments
- Fraud detection
- NLP
- Data pipelines
Cloud & DevOps
Deployable systems with sensible operational discipline.
- Cloudflare
- Docker
- DigitalOcean
- GitHub Actions
Product & UX
Discovery, user flows, design systems, and performance-minded delivery.
- Product scoping
- Responsive UI
- Design QA
- Iteration
I reduce ambiguity into artifacts teams can build from.
This is the part recruiters rarely see in a code sample: how I turn an idea into system boundaries, interface behavior, and delivery work.
Problem brief
Who uses it, what breaks today, what decision the product must make easier.
System model
Entities, API contracts, states, failure paths, and what belongs on the client or server.
Product surface
The screens, copy, empty states, loading states, and review moments that make the system usable.
Delivery path
Build, deploy, verify, document, and leave the next iteration easier than the first one.
Tools I reach for when building modern products.
The stack is intentionally practical: strong defaults, broad ecosystem support, and deployment paths that do not fight the product.
Python, FastAPI, React, Next.js, TypeScript, Supabase, PostgreSQL, Docker, Cloudflare, Salesforce automation.
Writing that supports the portfolio story.
Short notes on product engineering, backend/frontend tradeoffs, deployment, and AI/data projects.
Product Engineering5 min read
What I Look For When Building Products End to End
A practical note on moving from idea to useful software: problem framing, data shape, interface quality, deployment, and iteration.
Engineering6 min read
Why Python APIs and Next.js Still Make a Strong Product Stack
How I think about splitting responsibilities between backend services, product UI, server actions, and deployment constraints.
AI/Data4 min read
Fraud Detection Is a Product Problem, Not Only a Model Problem
A useful fraud detection workflow needs review states, explainability, data quality, and user trust around the prediction.
I am strongest where product ambiguity meets technical execution.
I work across frontend, backend, automation, cloud, and AI/data experiments, but the common thread is product usefulness: what should exist, how it should behave, and how to build it without creating a maintenance trap.
I care about clear systems, readable interfaces, honest tradeoffs, and the details that make software credible to both users and engineering teams.
AboutLet’s build useful software with strong product and engineering judgment.
Software engineering roles, full-stack and backend work, product engineering teams, and AI/data-oriented projects.
- Full-stack product features
- Python / FastAPI services
- React / Next.js interfaces
- Workflow automation
- AI/data product workflows