Technical expertise
Organised by what the technology is for
Grouped by engineering domain rather than dumped into one wall of logos. Where the record documents the context a technology was used in, that context is shown, instead of an arbitrary proficiency percentage, which tells a reader nothing.
Backend
Where most of the work lives: services that hold state, talk to money, and have to be right.
- JavaFIAP Transfer Service, BAP Reporting Service
- Spring BootMulti-bank funds transfer, BAP/BRS
- Node.jsBiller Aggregation Platform, Abiapay microservices
- PHPIn-house property platform microservices
- LaravelVAS biller integrations, Card Support Portal, freight system
- PythonNLP tax-compliance model, MCP server
- CodeIgniterTechnovia web & mobile estate
Also works with
- CakePHP
- Django
- Flask
Frontend & Mobile
Client surfaces shipped to real users on the web and to both mobile app stores.
- Next.jsProperty platform website
- React NativeProperty platform app, iOS and Android
- KotlinTruckka freight companion app
- IonicTechnovia mobile applications
Also works with
- React.js
- TypeScript
- JavaScript
- HTML5
- CSS3
Architecture
Decisions about boundaries: what becomes a service, what stays a module, and how the parts talk.
- Microservices ArchitectureProperty platform, Abiapay, BAP
- RESTful API DesignB2B middleware integration surface
- Mobile App Delivery (iOS & Android)React Native and Kotlin releases
- Third-Party API IntegrationPartner banks, MoMo, 14+ VAS billers, tracking APIs
Also works with
- Distributed Systems
Databases & Data
Relational where correctness matters, document and search where shape varies.
Also works with
- MySQL
- PostgreSQL
- MongoDB
- Redis
- Elasticsearch
- CouchDB
- SQL
Cloud & DevOps
End-to-end ownership of how code reaches production and stays reachable once it is there.
- DockerContainerised property platform services
- CI/CDPipelines built and owned for the property platform
- AWS S3Platform storage layer
Also works with
- Jenkins
- AWS
- DigitalOcean
- Git
Applied AI / Machine Learning
Models and AI tooling treated as a production concern: serving, latency and integration, not notebooks.
- TensorFlow ServingInference optimisation for engagement prediction
- Applied Machine LearningUser-engagement prediction model
- NLPAutomated tax-compliance insight extraction
- MCP ServersModel Context Protocol server built in Python
Engineering Practice
How the work gets planned, sequenced, reviewed and handed over.
- Technical Team LeadershipTeam Lead and Assistant Team Lead appointments
- Agile DeliveryAgile iteration cycles at Truckka Logistics
- Sprint Planning & Delivery CoordinationEngineering leadership team, Baxi by Onafriq
- MentoringJunior developers, Baxi by Onafriq
Also works with
- Scrum
- Kanban
Additional Languages
Listed on the CV alongside the above; used less centrally than the primary stack.
Also works with
- Go
- C#
- Rust
Why there are no percentage bars
A skills chart claiming “Java 95%” is unfalsifiable. It has no unit, no scale and no evidence behind it, and every engineer’s bar chart looks the same. It tells a hiring manager nothing they can evaluate.
The context notes above are checkable instead. Where a technology has a system named beside it, that system appears on my CV and, where relevant, in a case study. If it is listed without one, it is a technology I work with that does not have a named production system attached, and saying so is more useful than inventing one.
Contact
Let’s Build Something Meaningful
I’m open to senior and lead engineering roles internationally, backend, full-stack, architecture and platform work. The fastest route is email; LinkedIn works just as well.