CTO / Software Engineer / ML Engineer
Building intelligent systems, multi-tenant operating systems, and machine learning-powered execution layers for real-world products.
Years coding
6+
GitHub projects
30+
ML records processed
45K+
Live systems pulse
Operating model
RealtimeArchitecture loop
Selected systems
Production-minded work
Harlem Manage
Built the full backend of a multi-tenant proptech platform: 46-tool role-based AI agents, real-time M-Pesa WebSocket payment flows, a 3-queue Celery task system, and a PII-scrubbing observability stack.
CodePinion
An open-source developer Q&A platform built across 3 collaboration modes — async threads, real-time chat, and integrated video calls — moving knowledge sharing from static forum searches to live problem solving.
Catalog-Point
A full-stack library operations system with 5 core relational models, 2 user role types, a borrowing transaction engine with date-based cost calculation, and a deployment-ready Django stack.
E-Commerce Backend System
A backend commerce API built across 3 service domains (catalog, cart, order) with JWT-authenticated role-aware authorization, a relational schema optimized for checkout and order lifecycle workflows, and a separate React frontend — 2 public repos.
ML Search Engine
A 2-stage hybrid retrieval system trained on 45,000 StackOverflow records — SGD-based tag prediction for query expansion feeding into TF-IDF vectorization with cosine similarity ranking.
Movie Recommendation System
A 3-stage content-based recommendation pipeline — metadata extraction, vector representation, and cosine similarity scoring — that generates explainable suggestions with no user interaction data required.
46
AI agent tools across 4 user roles
3
Celery queue types over RabbitMQ
20+
PII field patterns scrubbed from error reports
45K+
ML records processed end-to-end
6+
Years coding, since 2020
30+
GitHub projects across 3 domains
I focus on the kind of engineering that holds up under real use: multi-tenant architecture, robust data flows, role-aware systems, and software that can be reasoned about, debugged, and improved over time.
Current domain
Proptech operating systems
Leading Harlem Manage as a Kenya-first multi-tenant real estate operating system with embedded AI execution.
Production background
Health + fintech workflows
Experience in structured, reliability-sensitive systems that shaped how I approach financial correctness and backend design.
Engineering style
Systems thinking, scalable backend design, and AI that behaves like a governed product subsystem instead of a bolt-on feature.
These projects highlight how I approach architecture, domain problems, and production-ready implementation.
Built the full backend of a multi-tenant proptech platform: 46-tool role-based AI agents, real-time M-Pesa WebSocket payment flows, a 3-queue Celery task system, and a PII-scrubbing observability stack.
Harlem Manage is a Kenya-first, multi-tenant real estate operating system built for landlords, agencies, and property firms. It combines property workflows, tenant and lease management, financial reconciliation, communication channels, and Akoko, a deployed operational intelligence layer that adapts by role.
46
AI agent tools
4
RBAC user roles
3
Celery queue types
Architecture highlights
An open-source developer Q&A platform built across 3 collaboration modes — async threads, real-time chat, and integrated video calls — moving knowledge sharing from static forum searches to live problem solving.
CodePinion is a developer Q&A platform designed to close the gap between the person asking and the person best positioned to help. Rather than forcing developers through slow async threads, it layers real-time chat and video calling on top of a persistent Q&A base so problems can be worked through in context.
3
Collaboration modes
Open source
Public GitHub repo
Full-stack JS
Frontend + backend
Architecture highlights
A full-stack library operations system with 5 core relational models, 2 user role types, a borrowing transaction engine with date-based cost calculation, and a deployment-ready Django stack.
Catalog-Point is a Django-based library management system covering the full operational surface of a real library: inventory tracking, category management, borrowing workflows, cost calculation, approval states, return handling, and user activity history — for both librarians and members.
5
Core relational models
2
User role types
Deployed
Gunicorn + WhiteNoise
Architecture highlights
A backend commerce API built across 3 service domains (catalog, cart, order) with JWT-authenticated role-aware authorization, a relational schema optimized for checkout and order lifecycle workflows, and a separate React frontend — 2 public repos.
A backend-first commerce platform focused on clear domain separation, predictable API behavior, and a schema that supports catalog, cart, and order lifecycles without coupling everything into a single service layer. Paired with a public React frontend repo.
3
Service domains
JWT + RBAC
Auth layer
2 repos
Frontend + backend
Architecture highlights
A 2-stage hybrid retrieval system trained on 45,000 StackOverflow records — SGD-based tag prediction for query expansion feeding into TF-IDF vectorization with cosine similarity ranking.
A machine learning search system built on ~45,000 StackOverflow records. The key insight was that a single retrieval technique misses intent — so the pipeline runs in 2 stages: classify the query to predict missing context tags, then use those enriched tags to improve the similarity search.
45K+
Training records
2-stage
Hybrid retrieval pipeline
SGD + TF-IDF
Model combination
Architecture highlights
A 3-stage content-based recommendation pipeline — metadata extraction, vector representation, and cosine similarity scoring — that generates explainable suggestions with no user interaction data required.
A content-based recommender that processes movie metadata through 3 explicit pipeline stages: feature extraction, vector representation, and similarity scoring. The design prioritizes explainability — every suggestion is traceable to specific shared metadata signals rather than opaque collaborative filtering.
3
Pipeline stages
Content-based
No user data needed
Explainable
Traceable recommendations
Architecture highlights
The combination of CTO-level product building, informatics systems, and fintech experience informs how I think about data integrity, workflows, and software reliability.
Current
Backend Engineer / Full-Stack Python Engineer
Current
Software Engineer
3 months
Software Engineer Intern / Contract
Capabilities
Backend Engineering
REST API Design / Systems Design / Django / PostgreSQL / MySQL / Redis / RabbitMQ
ML / Data Science
TF-IDF & NLP / Classification / Similarity Models / scikit-learn / TensorFlow / OpenAI API
Programming
Data Structures & Algorithms / Python / TypeScript / JavaScript / Java / React / Next.js / HTML / CSS
Cloud & Infrastructure
AWS / EC2 / PostgreSQL RDS / S3 / Object Storage / Linux Server Administration / Nginx / Gunicorn / Daphne / SSL & Server Hardening / systemd / CI/CD & Deployment Automation / Grafana & Loki
Tools
Git / Linux / Docker / Grafana / Sentry / GlitchTip / Codex / Claude Code
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